Selected Senior / Advanced Agentic AI Engineering Roles

Generated: 2026-07-05

Important note on exact job descriptions

This file does not reproduce full job descriptions verbatim. Job postings are copyrighted content, so the full exact text should be read through the official source links below. This file consolidates the selected roles into one Markdown document with:

official or best-available source links;
role metadata;
paraphrased job-description summaries;
key responsibilities and requirements;
why each role is strategically relevant for senior agentic-system engineering;
resume/application keywords to target.

For exact wording, open the source link for each role.

1. Cohere — Senior Software Engineer, Agent Infrastructure

Source: https://jobs.ashbyhq.com/cohere/70664617-84f6-4ee8-a4f6-4037ebfda9db
Company: Cohere
Role: Senior Software Engineer, Agent Infrastructure
Department / Area: Agentic Platform
Location: Toronto; Canada; United States
Work arrangement: Remote
Employment type: Full-time
Priority: Very high

Why this role is special

This is one of the closest matches to true agentic-system engineering: infrastructure for agents, not just application-level GenAI features. It aligns well with experience around LLM workflows, agent orchestration, context engineering, SDD, reusable commands, and AI-native engineering practices.

Paraphrased job-description summary

The role sits inside Cohere's agentic platform work and focuses on building infrastructure that enables agentic AI capabilities for enterprise use cases. The work likely involves reliable agent execution, platform services, integration patterns, and developer-facing primitives for agent workflows.

Likely responsibility themes

Build agent infrastructure and platform capabilities for enterprise LLM/agent products.
Support reliable execution of agent workflows.
Work on scalable systems that connect models, tools, context, and user workflows.
Collaborate with product, research, and applied AI teams.
Improve reliability, latency, observability, and maintainability of agent-platform services.

Strong resume/application keywords

agent infrastructure, agentic platform, LLM orchestration, tool calling, context engineering, enterprise AI, RAG, agent workflows, distributed systems, developer productivity

Fit notes

Excellent fit if positioning yourself as a Principal/Senior engineer who can lead internal AI-native engineering adoption and also design reusable systems behind agent workflows.

2. Docker — Staff Software Engineer, Agentic Platform

Source: https://jobs.ashbyhq.com/docker/348e2a4c-f794-4106-8c36-bb313ff15819
Company: Docker
Role: Staff Software Engineer, Agentic Platform
Location: Seattle, WA
Work arrangement: Remote
Employment type: Full-time
Compensation signal found in search results: approximately USD $170,350–$275,550
Priority: Very high

Why this role is special

This role is highly differentiated because Docker is positioned around secure, containerized execution for agents. This is not generic GenAI product work; it touches agent runtime safety, sandboxing, MCP tooling, orchestration, developer workflows, and evaluation.

Paraphrased job-description summary

The role focuses on building Docker's agentic platform capabilities, especially where AI agents interact with developer environments, containers, secure execution boundaries, and platform tooling. Search results mention MCP tooling, Docker/Kubernetes, secure code execution environments, LLM-as-judge evaluation, behavioral regression testing, and golden datasets.

Responsibility themes

Build platform components for agentic developer workflows.
Design or integrate MCP servers and agent tool interfaces.
Work with containers, Kubernetes, sandboxing, and secure execution environments.
Support evaluation systems such as LLM-as-judge, regression testing, and golden datasets.
Build infrastructure for event-driven agent workflows, state, cache, and pub/sub.
Collaborate across Docker's product ecosystem such as Docker Desktop, Docker Hub, and Docker Scout.

Strong resume/application keywords

MCP, agent sandboxing, secure execution, Docker, Kubernetes, LLM-as-judge, agent evaluation, behavioral regression testing, golden datasets, developer tooling, agent runtime

Fit notes

One of the strongest roles if you want to build something special and infrastructure-heavy. Your resume should emphasize secure AI-native SDLC, reusable agent workflows, validation guardrails, and engineering platform leadership.

3. Redcan.ai — Staff Software Engineer, Agentic AI Products

Source: https://ca.linkedin.com/jobs/view/staff-software-engineer-%E2%80%93-agentic-ai-products-at-redcan-ai-4397611665
Alternative source: https://bettercareer.ca/job/staff-software-engineer-agentic-ai-products/
Company: Redcan.ai
Role: Staff Software Engineer – Agentic AI Products
Location: Waterloo, Ontario, Canada
Work arrangement: Remote / Canada-based signal
Employment type: Full-time
Compensation signal found: CAD $140,000–$190,000 on BetterCareer; another listing showed a lower range, so verify directly before applying.
Priority: Very high

Why this role is special

This is directly about building agentic AI products, not merely adding LLM calls to an existing app. It is also Canada-based, senior, product-facing, and aligned with leadership across product, engineering, and AI-enabled SDLC transformation.

Paraphrased job-description summary

Redcan builds agentic AI products and tools for enterprise software deployment and configuration. The Staff Software Engineer role is responsible for shaping product architecture, building production-grade systems, contributing across frontend/backend domains, leading technical discovery, and helping teams use AI-enabled tools and SDLC transformation practices.

Responsibility themes

Build scalable applications using TypeScript, React, and Node.js.
Use Docker and Infrastructure as Code where appropriate.
Lead stakeholder discovery and translate business needs into engineering plans.
Own features end to end, from problem identification to design, implementation, monitoring, and iteration.
Mentor engineers and influence technical direction.
Contribute to product roadmaps and identify opportunities for automation, scalability, and AI-enabled innovation.
Work with LLM APIs, AI-enabled coding tools, and agent orchestration tools.

Requirements / qualification themes

8+ years of full-stack or backend engineering experience.
Strongly typed programming experience.
Scalable system design, cloud-native architecture, and DevOps exposure.
Strong ownership, communication, and leadership.
Authorization to work in Canada without sponsorship.
Experience with current AI-driven tools, LLM APIs, AI-enabled development tools, or ML-adjacent development.

Strong resume/application keywords

agentic AI products, AI-enabled SDLC, LLM APIs, agent orchestration, TypeScript, React, Node.js, Docker, Infrastructure as Code, technical discovery, product architecture, engineering leadership

Fit notes

Very good for your profile because it combines product management, engineering leadership, AI-native adoption, and practical production software delivery.

4. Wolters Kluwer — Senior Full Stack Engineer, AI Platform & Agents

Source: https://wk.wd3.myworkdayjobs.com/en-US/External/job/Senior-Full-Stack-Engineer--AI-Platform---Agents_R0052281
Company: Wolters Kluwer
Role: Senior Full Stack Engineer, AI Platform & Agents
Location: US/Canada hybrid/remote signal found
Employment type: Full-time
Priority: High

Why this role is special

This role is enterprise GenAI platform work for domains such as healthcare, legal, tax, and compliance. It is relevant if you want production AI platform experience in regulated, knowledge-heavy environments.

Paraphrased job-description summary

The role focuses on building a GenAI platform and agent tooling that supports critical professional decision workflows. Search results indicate AI agent development and evaluation, backend development, frontend development, and cloud services across AWS/Azure/GCP.

Responsibility themes

Build AI platform and agent capabilities.
Develop backend services, frontend capabilities, and cloud services.
Support reusable AI-agent delivery tooling.
Contribute to AI-agent development, evaluation, and deployment.
Work across professional/regulated domains where correctness and reliability matter.

Strong resume/application keywords

AI platform, AI agents, agent evaluation, full-stack engineering, cloud services, AWS, Azure, GCP, regulated domains, enterprise GenAI

Fit notes

Good target if you want a stable enterprise role with practical GenAI platform engineering rather than frontier AI research.

5. RBC — Staff Engineer, Agentic AI

Source: https://jobs.rbc.com/ca/en/job/RBCAA0088R0000175847EXTERNALENCA/Staff-Engineer-Agentic-AI
Company: Royal Bank of Canada
Role: Staff Engineer — Agentic AI
Location: Toronto, Ontario, Canada
Status observed: The role page indicated the job had been filled when checked.
Priority: Track similar RBC roles, but this exact posting may no longer be active.

Why this role is still relevant

Even if filled, it is useful as a target archetype. RBC is actively investing in AI engineering, agentic patterns, AI/ML leadership, and internal technology platforms.

Paraphrased job-description summary

The role appears to have been a staff-level agentic AI engineering role in RBC's technology, analytics, and research organization. Search results also showed related RBC postings around agentic patterns, tools, analytics, security AI/ML products, and AI/ML engineering.

Resume/application keywords for similar RBC roles

agentic AI, AI patterns, AI/ML platform, banking technology, enterprise governance, risk controls, secure AI, technical leadership, Toronto

Fit notes

Monitor RBC for new Staff Engineer, Lead AI Engineer, Director AI/ML Products, and Agentic Patterns roles. These are good if you want a local Toronto enterprise-AI track.

6. Citi — Lead Agentic AI Engineer – VP

Source: https://jobs.citi.com/job/mississauga/lead-agentic-ai-engineer-vp-mississauga/287/95890780496
Company: Citi
Role: Lead Agentic AI Engineer – VP
Location: Mississauga, Ontario, Canada
Work arrangement: Hybrid
Posted: 2026-06-02
Salary signal: CAD $120,800–$170,800
Priority: Very high if hybrid Mississauga works for you

Why this role is special

This is one of the most concrete and detailed agentic AI engineering postings found. It covers multi-agent systems, LLM providers, Google ADK, LangChain/LangGraph, RAG, vector databases, prompt/context management, secure APIs, MLOps, CI/CD, and leadership.

Paraphrased job-description summary

Citi is seeking a hands-on VP-level engineer to design, build, and deploy agentic AI solutions for wholesale banking technology. The role combines architecture ownership, technical leadership, rapid MVP delivery, and production-grade AI capabilities for automation, efficiency, and risk reduction.

Responsibility themes

Lead design, development, and deployment of large-scale agentic AI systems.
Architect multi-agent systems covering perception, reasoning, planning, and execution.
Integrate LLM providers such as OpenAI, Anthropic, Gemini, and open models.
Build with Google Gemini, Vertex AI, Google ADK, vector databases, RAG, semantic search, and prompt/context management.
Engineer autonomous agents with planning, tool usage, memory, and multi-step reasoning.
Develop backend services with Python, FastAPI, asyncio, event-driven design, microservices, and APIs.
Work with SQL, NoSQL, secure REST APIs, OAuth, authorization, and encryption.
Optimize cost, latency, prompts, caching, and vector indexes.
Establish AI lifecycle practices across prompt engineering, model evaluation, MLOps, and data management.
Mentor AI/ML engineers and collaborate with business units.

Requirements / qualification themes

6–10 years of relevant engineering experience.
At least 2+ years in AI, prompt engineering, ML, or agentic AI systems.
Strong Python experience, including FastAPI/Django/Flask and concurrency.
Java, TypeScript/JavaScript, SQL, cloud, Docker, Kubernetes, and CI/CD.
LLM and agent-framework experience: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Google ADK.
RAG and vector database experience: Pinecone, Weaviate, FAISS, pgvector, ChromaDB.
MLOps, evaluation, lifecycle management, and secure distributed systems.

Strong resume/application keywords

Google ADK, LangGraph, LangChain, multi-agent systems, OpenAI, Anthropic, Gemini, Vertex AI, RAG, semantic search, context management, FastAPI, MLOps, agent memory, tool use, OAuth, Kubernetes

Fit notes

Very strong match if you want to target enterprise-scale agentic AI. Your resume should explicitly mention LLM providers, agent workflows, context engineering, prompt standards, validation, and secure AI delivery.

7. Citi — Agentic AI Technical Lead

Source: https://jobs.citi.com/job/mississauga/agentic-ai-technical-lead/287/97223844272
Company: Citi
Role: Agentic AI Technical Lead
Location: Mississauga, Ontario, Canada
Work arrangement: Hybrid
Posted: 2026-07-02
Salary signal: CAD $120,800–$170,800
Priority: Very high

Why this role is special

This is probably the most directly relevant posting for building an internal enterprise agentic AI platform. It explicitly mentions multi-tenant agentic ecosystems, custom fine-tuning applications, MCP servers, ReAct/ReWOO, ADK, GraphRAG, LightRAG, RAPTOR, Neo4j, pgvector, prompt/intent engineering, and forward-deployed engineering.

Paraphrased job-description summary

Citi is hiring a hands-on technical lead for its Services AI Platform engineering team. The person will split time between production coding and technical strategy, building multi-tenant agentic ecosystems and platform components aligned with trust, adoption, cost, operations, and scalability.

Responsibility themes

Lead development of AI platform components using Python and Angular.
Build internal fine-tuning planes and experiment-tracking dashboards.
Code and optimize multi-tenant agents using ReAct, ReWOO, and Google ADK-style orchestration.
Build MCP servers with clean integration boundaries between agents, APIs, and enterprise data sources.
Implement advanced retrieval architectures including GraphRAG, LightRAG, and RAPTOR-style hierarchical summaries.
Integrate graph and vector databases such as Neo4j and pgvector.
Design prompt and intent-classification logic to avoid workflow collisions.
Work directly with SMEs to convert enterprise workflows into automated agent-driven code.
Connect backend agentic logic to UI, workflows, and existing enterprise APIs.

Requirements / qualification themes

10+ years of experience.
5+ years in AI/ML and 2+ years in Generative AI.
3+ years of technical leadership.
AWS AI/ML infrastructure experience.
Production AI solution delivery portfolio.
Computer Science, Data Science, AI, or equivalent background.

Strong resume/application keywords

MCP servers, ReAct, ReWOO, Google ADK, GraphRAG, LightRAG, RAPTOR, Neo4j, pgvector, fine-tuning platform, experiment tracking, multi-tenant agents, prompt engineering, intent classification, forward deployed engineering

Fit notes

This is an excellent role to model your resume against. It rewards exactly the language you have been developing: agentic systems, context engineering, reusable commands/workflows, prompt standards, validation, and cross-functional adoption.

8. Citi — Agentic AI Tech Lead

Source: https://jobs.citi.com/job/mississauga/agentic-ai-tech-lead/287/97223844032
Company: Citi
Role: Agentic AI Tech Lead
Location: Mississauga, Ontario, Canada
Work arrangement: On-site / resident
Posted: 2026-07-02
Salary signal: CAD $120,800–$170,800
Priority: High if on-site is acceptable

Why this role is special

This appears very similar to the hybrid Agentic AI Technical Lead role but with on-site/resident work arrangement. It is still technically strong and highly relevant, especially for building enterprise agentic platforms.

Paraphrased job-description summary

The role is a hands-on technical lead position for Citi's Services AI Platform engineering team. It emphasizes production coding, technical strategy, multi-tenant agentic ecosystems, fine-tuning applications, clean MCP-based architecture, advanced RAG, and enterprise workflow automation.

Responsibility themes

Build AI platform applications with Python and Angular.
Develop multi-tenant intelligent agents using ReAct/ReWOO-style orchestration and Google ADK.
Build MCP servers and enforce clean data-access boundaries.
Implement GraphRAG, LightRAG, and RAPTOR-style retrieval approaches.
Integrate Neo4j and pgvector.
Design prompt and intent-engineering logic.
Work with SMEs as a forward-deployed engineer to automate enterprise workflows.

Requirements / qualification themes

10+ years of experience.
5+ years AI/ML and 2+ years GenAI.
3+ years leadership.
AWS AI/ML infrastructure.
Proven production AI delivery.

Strong resume/application keywords

agentic AI platform, MCP, multi-tenant agents, Google ADK, GraphRAG, LightRAG, RAPTOR, Neo4j, pgvector, Python, Angular, FDE, enterprise AI

Fit notes

Apply only if the on-site arrangement works. Otherwise, use it as a JD reference for tailoring to the hybrid Citi role.

9. LiveKit — Staff Rust SDK Engineer

Source: https://jobs.ashbyhq.com/LiveKit/a1d10340-7cd9-4029-9a56-139232f4bd5d
Company: LiveKit
Role: Staff Rust SDK Engineer
Location: Remote, U.S.; Remote, Canada
Work arrangement: Remote
Employment type: Full-time
Priority: Medium-high, especially if interested in real-time AI agents

Why this role is special

LiveKit is relevant because many agentic AI products are moving toward real-time voice, video, and multimodal interaction. This is more infrastructure/SDK-oriented than business-app GenAI.

Paraphrased job-description summary

The role focuses on Rust SDK engineering for LiveKit's real-time communications platform. Search results indicate that LiveKit's platform supports developers building, testing, deploying, scaling, and observing AI agents in production.

Responsibility themes

Build and maintain Rust SDKs.
Work on real-time communication infrastructure.
Support production-grade developer experience for voice/video/agentic applications.
Improve reliability, performance, and observability.

Strong resume/application keywords

Rust, SDK engineering, real-time systems, AI agents, voice agents, WebRTC, observability, developer experience, production agents

Fit notes

Strong if you want agent infrastructure in real-time/multimodal systems. Less directly aligned if your current resume is not Rust-heavy.

10. Juniper Square — Staff Software Engineer (AI)

Source: https://jobs.ashbyhq.com/junipersquare/c0f6d8c5-7dad-4599-be77-b04b8a2b8af0
Company: Juniper Square
Role: Staff Software Engineer (AI)
Location: Americas: USA or Canada signal found
Work arrangement: Remote signal found
Priority: High

Why this role is special

This is a strong senior AI platform role because it explicitly mentions AI SDKs, guardrails, evaluation frameworks, feedback systems, and agentic workflow infrastructure. That makes it highly aligned with building reliable internal AI systems rather than simple chatbot features.

Paraphrased job-description summary

The role appears to focus on AI platform and infrastructure for product teams, including reusable AI building blocks, guardrails, evaluation systems, feedback loops, and agentic workflow infrastructure.

Responsibility themes

Build AI platform infrastructure and reusable SDKs.
Design guardrails, evaluation frameworks, and feedback systems.
Support agentic workflow infrastructure.
Work across product and platform boundaries.

Strong resume/application keywords

AI SDKs, guardrails, evaluation frameworks, feedback systems, agentic workflow infrastructure, platform engineering, Staff Engineer, product AI

Fit notes

Excellent fit for a resume emphasizing AI governance, validation loops, reusable agent workflows, and platform thinking.

11. Temporal — Staff Software Engineer, AI Foundations

Source: https://job-boards.greenhouse.io/temporaltechnologies/jobs/5125079007
Company: Temporal Technologies
Role: Staff Software Engineer, AI Foundations
Location: United States, Remote Opportunity
Work arrangement: Remote
Compensation signal: USD $224,000–$302,400
Priority: Very high, especially for durable execution / workflows / agents

Why this role is special

This is one of the most technically interesting roles for agentic systems. Temporal is directly relevant to reliable agent execution, durable workflows, long-running tasks, retries, orchestration, and agent-framework integration.

Paraphrased job-description summary

Temporal is hiring for its AI Foundations team to accelerate adoption across AI applications, including agents and data pipelines. The team works on agentic coding skills for tools like Codex and Claude Code and plugin systems that connect Temporal's durable execution model with AI frameworks such as Pydantic AI, Vercel AI SDK, Google ADK, and OpenAI Agents SDK.

Responsibility themes

Build trusted agentic coding systems.
Design and implement Temporal AI SDK features across multiple frameworks.
Develop AI application patterns and architectures.
Own features end to end while improving reliability and developer experience.
Work in Python, TypeScript, Java, and Go.
Serve as a domain expert for AI design patterns.
Support developer community debugging and public technical documentation.

Requirements / qualification themes

8+ years of professional engineering experience.
Strong interest in generative AI, agents, and coding systems.
Emphasis on using AI to increase quality, not just output volume.
Strong code quality and software design judgment.
Open-source contribution experience.
Fluency in multiple programming languages.
Deep concurrency experience.
API design and documentation experience.

Strong resume/application keywords

durable execution, agent orchestration, AI SDK, Codex, Claude Code, OpenAI Agents SDK, Google ADK, Pydantic AI, Vercel AI SDK, developer experience, concurrent programming, open source, agentic coding systems

Fit notes

This is a strong stretch role if you want to build foundations for reliable agents. It aligns with your interests in distributed systems, workflow orchestration, and LLM coding agents.

12. OpenAI — Software Engineer, Agent Infrastructure

Source: https://openai.com/careers/software-engineer-agent-infrastructure-san-francisco/
Company: OpenAI
Role: Software Engineer, Agent Infrastructure
Location: San Francisco / 2-location signal from OpenAI search page
Priority: Stretch target

Why this role is special

This is frontier agent-infrastructure work at the model/product boundary. It is less Canada-friendly but strategically valuable as a benchmark for how top labs describe agent infrastructure.

Paraphrased job-description summary

The role focuses on infrastructure for training and deploying AI-agent systems. OpenAI's role page highlights collaboration with research teams, building systems for novel training runs and experimental applications, large-scale ML infrastructure, rapid 0-to-1 execution, and scaling systems dramatically.

Responsibility themes

Build and optimize infrastructure for AI training and agent-related applications.
Collaborate closely with research teams.
Identify system bottlenecks and engineer scalable solutions.
Work on large-scale ML systems and experimental deployments.

Strong resume/application keywords

agent infrastructure, large-scale ML infrastructure, AI training systems, research engineering, systems optimization, 0-to-1, scale, experimental applications, frontier AI

Fit notes

Useful as a stretch benchmark. Your profile would need stronger emphasis on large-scale ML infrastructure, distributed systems, and production infra rather than only prompt/workflow leadership.

13. OpenAI — Security Engineer, Agent Security

Source: https://openai.com/careers/security-engineer-agent-security-san-francisco/
Company: OpenAI
Role: Security Engineer, Agent Security
Location: San Francisco / 2-location signal from OpenAI search page
Priority: Stretch target; excellent strategic match for AI + cybersecurity

Why this role is special

This role combines agentic AI and security engineering. It is directly aligned with secure agent deployment, control frameworks, policy, and threat modeling for agentic systems.

Paraphrased job-description summary

The role focuses on securing agentic AI systems by designing and implementing security frameworks, policies, and controls that protect assets and support safe deployment of agentic systems.

Responsibility themes

Secure agentic AI systems.
Design security frameworks, controls, and policies.
Protect critical assets.
Support safe deployment of agentic AI capabilities.
Likely work with product, research, infra, and security stakeholders.

Strong resume/application keywords

agent security, secure AI, threat modeling, security controls, policy, agentic systems, AI safety, secure deployment, governance, cybersecurity

Fit notes

Very good conceptual fit with your security-engineering goals. To target similar roles, strengthen your resume around threat modeling for AI systems, MCP/tool-call risk, data exfiltration, prompt injection, sandboxing, and human-in-the-loop controls.

Source: https://www.anthropic.com/careers/jobs
Company: Anthropic
Relevant roles found on careers page:

Engineering Manager, Agent Prompts & Evals
Model Performance Software Engineer, Claude Code
Staff Software Engineer, Developer Productivity (CI/CD) - Claude Code
Staff Software Engineer, Developer Productivity (Dev Environments) - Claude Code
Staff Software Engineer, AI Reliability
Engineering Manager, Agent Runtime Platform

Locations shown in careers page results: mostly San Francisco, New York City, Seattle, and some remote-friendly/travel-required roles depending on position.
Priority: Stretch target / research-oriented benchmark

Why these roles are special

Anthropic's relevant openings point to two major areas: agent prompts/evaluation and Claude Code developer-productivity infrastructure. These are directly relevant to agentic coding systems, evaluation, reliability, model behavior, and production AI developer tooling.

Paraphrased job-description summary

The careers page lists multiple roles around agent prompts, evals, Claude Code, developer productivity, AI reliability, agent runtime platform, safeguards, and platform engineering. These roles are more frontier-lab-oriented than typical enterprise AI roles.

Responsibility themes to expect

Improve agent prompts and evaluations.
Build developer productivity infrastructure for Claude Code.
Improve AI reliability and platform capabilities.
Build systems around agent runtime, evals, CI/CD, and dev environments.
Work at the boundary of product engineering, AI behavior, and model evaluation.

Strong resume/application keywords

agent prompts, agent evals, Claude Code, developer productivity, AI reliability, agent runtime, model performance, eval harness, prompt engineering, LLM behavior, CI/CD for AI tools

Fit notes

Use Anthropic postings as a benchmark for how to frame advanced agentic-system work. Your resume should emphasize evaluation, prompt standards, context engineering, reproducibility, safety, and AI-native developer workflows.

Best application order

Citi — Agentic AI Technical Lead
Citi — Lead Agentic AI Engineer – VP
Docker — Staff Software Engineer, Agentic Platform
Cohere — Senior Software Engineer, Agent Infrastructure
Temporal — Staff Software Engineer, AI Foundations
Redcan.ai — Staff Software Engineer, Agentic AI Products
Juniper Square — Staff Software Engineer (AI)
Wolters Kluwer — Senior Full Stack Engineer, AI Platform & Agents
OpenAI — Security Engineer, Agent Security
OpenAI — Software Engineer, Agent Infrastructure
Anthropic — Agent Prompts & Evals / Claude Code roles
LiveKit — Staff Rust SDK Engineer
RBC — Track future Staff/Lead Agentic AI roles

Resume positioning to use across these roles

Principal / Staff-level headline

Principal Software Engineer focused on AI-native software delivery, agentic AI systems, LLM-enabled engineering workflows, context engineering, prompt standards, Spec-Driven Development, and secure human-in-the-loop validation.

Strong resume bullet

Led the design and adoption of internal agentic AI systems for engineering teams, combining LLM orchestration, Spec-Driven Development, context engineering, prompt standards, reusable agent workflows, tool integration, and human-in-the-loop validation to improve AI-native software delivery.

More technical resume bullet

Architected agentic LLM workflows with structured planning, tool invocation, context retrieval, prompt templates, validation loops, and reusable commands to support requirement discovery, implementation planning, code review, test generation, and delivery governance.

Keywords to emphasize

agentic AI systems, agentic platform, LLM orchestration, MCP, tool calling, context engineering, prompt engineering, Spec-Driven Development, human-in-the-loop validation, RAG, GraphRAG, LightRAG, RAPTOR, agent evaluation, LLM-as-judge, guardrails, secure execution, sandboxing, developer productivity, AI-native SDLC, Claude Code, Codex, Google ADK, LangGraph, LangChain, OpenAI Agents SDK, AI SDK, Temporal, Docker, Kubernetes

15. AI / ML Engineer — Enterprise ML + Generative AI (Riyadh, onsite)

Company: (enterprise / consulting, KSA) · Location: Riyadh (onsite) · Experience: 3–11 years · Priority: High (added 2026-07-11)

Paraphrased summary

Design, develop, deploy, and optimize ML/DL and generative-AI solutions end to end: build scalable models, production ML pipelines (ingestion → training → evaluation → deployment), and LLM-powered apps, on cloud-native AI platforms, with MLOps discipline.

Required stack (explicit)

Cloud AI platforms: GCP Vertex AI or Azure Machine Learning or AWS SageMaker; Azure OpenAI or AWS Bedrock for GenAI; BigQuery ML + Dataflow.
Programming/ML: strong Python; TensorFlow or PyTorch; supervised / unsupervised / reinforcement learning / deep learning.
GenAI/LLM frameworks: Hugging Face + LangChain; prompt engineering, RAG, embeddings, vector databases (preferred).
Data engineering: Databricks; data preprocessing, feature engineering, large-scale processing.
MLOps/deploy: production deployment, model versioning + monitoring + CI/CD; Docker, Kubernetes (advantage).
Preferred: LLMs & GenAI apps, RAG + vector DBs + AI agents, distributed training, cloud-native AI architectures, cloud certs (AWS/Azure/GCP).

Coverage in this track

Phase 24 (Bedrock) + Part III Phases 25–28 (managed platforms · MLOps · data/feature engineering · Kubernetes/SRE) + Part IV Phases 29–32 (LangChain · Hugging Face · Cohere · ML/DL foundations) were added specifically to cover this role. RAG/embeddings/vector-DB = Phases 04–06; agents = Phases 01/07.

Keywords to emphasize

Vertex AI, Azure ML, SageMaker, Azure OpenAI, Bedrock, BigQuery ML, Dataflow, Databricks, TensorFlow, PyTorch, Hugging Face, LangChain, RAG, embeddings, vector database, feature engineering, feature store, MLOps, model registry, model monitoring, drift, CI/CD, Docker, Kubernetes, distributed training

16. Cohere — Forward Deployed Engineer, Agentic Platform (North) (Riyadh / Dubai / KSA / UAE)

Company: Cohere · Department: Agentic Platform (North) · Location: Middle East (Riyadh/Dubai; hybrid) · Type: Full-time, 20–40% travel · Priority: Very high (added 2026-07-11)

Paraphrased summary

Software engineer with applied-AI experience who owns the design → build → deploy of LLM-powered agentic workflows for enterprise customers — from prototype to production-grade agents that reason, plan, and act across tools, APIs, and sensitive data — and ships features for North, Cohere's secure AI-workspace platform. Customer-facing: translate ambiguous business problems into well-framed workflows with clear success criteria and evaluation methodology. Enterprise-high bar: reliable, observable, safe, auditable from day one.

What they listen for

Production-grade Python (clean, testable, observable, scalable).
Shipped high-performance RAG and agentic apps — multi-step planning with ReAct / Plan-and-Execute.
Deep familiarity with the LLM stack: frontier models, vector databases, orchestration frameworks.
Robust evaluation frameworks — measure agent accuracy, safety, latency beyond trial-and-error.
Customer-facing technical leadership; end-to-end use-case ownership; comfort flexing into any area (incl. frontend).
Bonus: architectural standards for agentic systems; regulated industries (finance/healthcare/telecom); enterprise security/compliance/auditability.

Coverage in this track

Core match: Phases 01 (ReAct/ReWOO/Plan-Execute), 05–06 (RAG + vector retrieval), 07 (multi-agent), 10 (security/guardrails), 11 (evaluation: accuracy/safety/latency), 13 (multi-tenant/auditability), 17 (capstone). Cohere-specific (Command/Embed/Rerank/North/grounded-generation-with-citations) added as Phase 31. Customer-facing framing lives in interview-prep/.

Keywords to emphasize

agentic platform, forward deployed engineer, RAG, ReAct, Plan-and-Execute, agent evaluation, accuracy / safety / latency, vector database, orchestration frameworks, Cohere Command, Rerank, Embed, grounded generation, citations, enterprise security, auditability, Python

17. AI Platform SRE — Kubernetes/OKE Operations, CI/CD & MLOps (KSA)

Company: (enterprise, KSA) · Experience: 3–8 years · Priority: High (added 2026-07-11)

Paraphrased summary

Operate and optimize production Kubernetes (Oracle OKE or equivalent) hosting AI/platform services; build and maintain CI/CD pipelines; own observability & incident response; coordinate model testing/validation and production deployment/rollback; run performance monitoring, drift reports, and retraining tickets.

Required stack (explicit)

Kubernetes/OKE: cluster provisioning, node pools, autoscaling, network policies, RBAC, secret management, disaster-recovery.
CI/CD: Jenkins / GitLab CI / GitHub Actions / Argo CD; static analysis, container image scanning (Trivy), unit/integration tests, staged artifact promotion, quality gates.
Observability/incident: metrics, alerts, dashboards, log aggregation, distributed tracing, on-call — Prometheus, Grafana.
Security/compliance: pod security standards, vulnerability remediation, image signing, compliance checks.
IaC: Terraform, Helm, GitOps.
Scripting: Bash / Python / Go (automation, custom operators).
Perf tuning: capacity planning, resource quotas, latency optimization, cost-efficient scaling.
Model ops: functional/performance/bias testing, model sign-off, rollout/rollback verification, drift-threshold retraining triggers.

Coverage in this track

Added as Phase 28 — Kubernetes & Cloud-Native AI Infrastructure (SRE) (reconciliation-loop orchestrator · CI/CD pipeline with gates + image scan + staged promotion + rollback · metrics/alerting/SLO engine), reinforced by Phase 26 (MLOps: model testing gate, drift → retraining) and Phase 14 (cost/latency/observability). Docker/sandboxing = Phase 09.

Keywords to emphasize

Kubernetes, OKE, node pools, autoscaling, network policies, RBAC, Helm, Terraform, GitOps, Argo CD, GitHub Actions, Jenkins, Trivy, image scanning, pod security, Prometheus, Grafana, distributed tracing, SLO, error budget, incident response, disaster recovery, model drift, retraining, Bash, Python, Go

Notes for applying

For Citi roles, tailor heavily to enterprise platform, secure architecture, MCP, RAG, and technical leadership.
For Docker, emphasize secure agent execution, sandboxing, MCP, containers, evaluation, and developer tooling.
For Cohere, emphasize agent infrastructure, platform reliability, enterprise AI, and scalable backend/distributed systems.
For Redcan, emphasize product ownership, full-stack leadership, TypeScript/React/Node.js, AI-enabled SDLC, and stakeholder discovery.
For Temporal, emphasize durable workflows, distributed systems, open source, concurrency, SDKs, and agentic coding systems.
For OpenAI/Anthropic, emphasize frontier-quality engineering, evals, reliability, safety, and research/product collaboration.
For the KSA AI/ML Engineer role, tailor to the full ML lifecycle on a named cloud platform (pick Vertex AI or Azure ML or SageMaker), plus GenAI (Bedrock/Azure OpenAI + LangChain/Hugging Face), data/feature engineering (BigQuery ML/Dataflow/Databricks), and MLOps (registry/monitoring/CI-CD/Docker/K8s). Consider a cloud cert.
For Cohere Forward Deployed Engineer, emphasize customer-facing delivery of production agentic workflows, RAG + ReAct/Plan-Execute, rigorous evaluation (accuracy/safety/latency), and enterprise auditability — and know Cohere's own Command/Embed/Rerank/North stack (Phase 31).
For the AI Platform SRE / OKE role, emphasize production Kubernetes operations, CI/CD with security gates and image scanning, IaC (Terraform/Helm/GitOps), observability (Prometheus/Grafana/tracing/SLOs), and model-ops (testing, rollout/rollback, drift → retraining).

Agentic AI Engineer — Agent Infrastructure, Platforms & Production Systems