Full-Stack Gen AI Engineer
Summary
Build and deploy generative AI applications using frameworks like TensorFlow and PyTorch, collaborating with clients to integrate AI into their systems.
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 |
|
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