Agentic AI Lead - Delivery & Engineering

Agentic AI Lead – Delivery & Engineering
Location: NYC, NY (3 days onsite)
Employment type: C2H
H1b consultants can apply, if they can convert to fulltime after 3 months

Job Description
About the Role
This is not a slide-making or prompt-engineering role. We are looking for someone who has built multi-agent AI systems that run in production - not demos, not pilots that died after a sprint. You will anchor AI delivery programs end-to-end, work directly with global clients, and stay sharp on a field that changes every few weeks.

You will report into and replicate the function of a senior AI delivery leader - which means you need both the depth to architect solutions and the presence to walk a CXO through what you built and why it works.

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

What You Must Have Actually Done

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

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

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

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