Gen AI Leads/Architect
Inficare, New York, NY, United States
Job Title:
Gen AI Leads/Architect
Location:
NYC, NY
Rate:
Keep it as low as possible
Job Description :
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
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
Gen AI Leads/Architect
Location:
NYC, NY
Rate:
Keep it as low as possible
Job Description :
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
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