Full-Stack Gen AI Engineer
Summary
Design, develop, and implement AI-powered applications and models using generative AI techniques, collaborating with cross-functional teams to translate business needs into technical solutions.
What You Must Have Actually Done
Not just what you know. What you have shipped.
• Deployed 2–3 agent-based systems in production - stateful, multi-step, real users
• Used LangGraph for multi-agent orchestration with memory, tool routing, and state management
• Built projects where AI (Claude Code, Codex, Cursor) wrote significant portions of the code
• Implemented RAG pipelines end-to-end - chunking, embedding, retrieval, re-ranking, evaluation
• Integrated agents with real enterprise APIs - not just OpenAI playground or sample data
• Debugged a production agent failure - and fixed it without blaming the model
• Can articulate when NOT to use agents - that is how we know you have built things
Bonus - Real Differentiators
• Experience with Claude Code CLI in team environments (CLAUDE.md, shared context, multi-session flows)
• Familiarity with LangSmith for agent tracing, evaluation pipelines, and debugging at scale
• Has shipped something using MCP (Model Context Protocol) or similar shared-context tooling
• QA/testing mindset for agents - systematic evaluation of non-deterministic outputs
• Background in IT services or consulting - managing client expectations while building
• Experience with SLMs, fine-tuning, or on-device/edge agent deployment
What We Are Not Looking For
• Someone who lists LLMs on a resume but has only called the API in a Jupyter notebook
• AI enthusiasts whose hands-on experience is less than a year old
• People who explain everything in terms of frameworks they have never deployed
• Consultants who can only narrate what others have built
Key Responsibilities
Delivery & Architecture
• Own end-to-end delivery of AI-native programs - from architecture through production deployment
• Design and build multi-agent orchestration systems using LangChain, LangGraph, CrewAI, or equivalent
• Integrate agent systems with enterprise surfaces: APIs, ERPs, CRMs, data platforms - not toy datasets
• Define agent topology: tool routing, memory strategy, state machines, fallback handling
Agentic Coding & Development
• Run agentic coding workflows using Claude Code, Cursor, OpenAI Codex, or equivalent CLI tools
• Lead projects where AI writes significant portions of the codebase - and you guide, review, and ship it
• Work with CLAUDE.md, shared context frameworks, and multi-session agent setups for team use
• Debug non-deterministic agent outputs systematically - not by gut feel
Client & Stakeholder Engagement
• Translate business problems into agent architectures for global CXO-level stakeholders
• Run discovery workshops, solution reviews, and delivery cadences with client teams
• Prepare and present technical proposals, POC plans, and roadmaps - own the story end-to-end
Team & Practice
• Mentor junior AI engineers; raise AI engineering quality across the delivery team
• Stay current: evaluate new models, frameworks, and tooling before the hype catches up
• Contribute to internal knowledge bases, reusable frameworks, and accelerators
Skills
Agent Orchestration LangChain, LangGraph, CrewAI - not just conceptual | Agentic Coding Tools | Claude Code CLI, Cursor, OpenAI Codex, Copilot |
RAG & Vector Stores Chroma, Weaviate, Pinecone - knows where RAG breaks | LLM APIs & SDKs | Anthropic, OpenAI, Gemini - prompt design, tool use |
Python / TypeScript Primary languages for agent + backend development | LangSmith / Observability Tracing, evaluation, debugging agent runs |
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Cloud Platforms Azure, AWS, GCP (at least one) - deployment, infra, managed services | API & System Integration | REST, gRPC, Kafka - enterprise integration patterns |
MCP / Shared Context Model Context Protocol, CLAUDE.md, Beads | Agent Evaluation | Testing non-deterministic outputs, guardrails, evals |
CI/CD & DevOps Git, containers, pipelines - agents need to ship | Client Communication | Can present architecture to a CXO without jargon |
How We Will Evaluate You
Not a theory round.
Expect to walk through something you have actually built - architecture decisions, what broke in
production, what you would do differently. If you cannot do that with specifics, this role is not the right
fit.
Evaluation stages:
• Stage 1 - Technical screen: Walk us through a live agent system you built
• Stage 2 - Architecture discussion: Given a business problem, design an agent solution on the spot
• Stage 3 - Stakeholder simulation: Present your approach to a non-technical executive audience