Full-Stack Gen AI Engineer

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