Lead design and build scalable autonomous and semi‑autonomous multi‑agent AI systems. Implement RAG pipelines, tool‑calling agents, memory, observability, and AgentOps. Ensure production readiness, safety, governance, and secure data handling. Provide technical leadership, mentor engineers, and shape platform standards across teams.
We are seeking a Lead Agentic AI Engineer with a strong software engineering foundation to design, build, and scale autonomous and semi‑autonomous AI systems. This role goes beyond prompt engineering or isolated models—you will architect multi‑agent systems that plan, reason, call tools, interact with enterprise systems, and operate safely at scale.
You will work on agent orchestration, RAG pipelines, tool‑calling, memory, observability, and AgentOps, while ensuring production readiness, compliance, and reliability.
Agentic AI System Design
- Design and implement multi‑agent architectures including:
- Orchestrator / supervisor agents
- Task‑specialized agents (research, extraction, validation, decisioning)
- Reflection, critique, and self‑correction loops
- Build goal‑driven agent workflows with constrained autonomy and human‑in‑the‑loop patterns.
- Define agent boundaries, decision policies, and escalation logic.
LLM, RAG & Tooling
- Build enterprise‑grade RAG systems:
- Ingestion, chunking, metadata enrichment
- Vector indexing and retrieval strategies
- Grounded generation and citation control
- Implement tool‑calling agents that interact with:
- APIs, databases, search systems
- Internal platforms and workflows
- Optimize latency, cost, and accuracy across LLM interactions.
Production Engineering & AgentOps
- Build agentic systems as scalable backend services (FastAPI / REST / async services).
- Apply strong software engineering discipline:
- Modular code, clean abstractions, testability
- CI/CD, versioning of prompts, tools, and agents
- Implement AgentOps / LLMOps, including:
- Evaluation harnesses (prompt, retrieval, agent behavior)
- Observability (traces, metrics, decision paths)
- Rollback and controlled rollout strategies
- Ensure robustness against hallucinations, loops, tool failures, and unsafe actions.
Governance, Safety & Reliability
- Implement guardrails, policies, and monitoring for agent behavior.
- Design systems with traceability, auditability, and explainability.
- Ensure secure handling of sensitive data (PII/PHI where applicable).
- Enforce responsible‑AI principles in autonomous systems.
Technical Leadership
- Lead design reviews and mentor junior engineers.
- Influence platform standards for agentic AI across teams.
- Partner with product managers, architects, and domain SMEs to translate workflows into agent behavior.
Strong Software Engineering Background (Must Have)
- 8–10 years of experience with backend/software engineering
- Expert in Python (primary) and API‑based services
- Experience with distributed systems, microservices, async processing
- Strong understanding of system design, scalability, and performance
Agentic AI & GenAI Expertise
- Proven experience building agentic AI systems (not just chatbots)
- Hands‑on with agent frameworks (e.g., LangChain / LangGraph / equivalent)
- Strong understanding of:
- Tool‑calling, planning, reflection
- Memory, state, and long‑running agents
- Experience working with LLMs (cloud and/or open‑source)
RAG & Data Engineering
- Production experience with vector databases
- Knowledge of embeddings, retrieval strategies, reranking
- Comfortable with structured + unstructured enterprise data
Platform & Ops
- Docker, CI/CD, cloud deployments (AWS / Azure / GCP)
- Experience with monitoring, logging, and production debugging
- Familiarity with MLflow or similar experiment tracking tools
Nice to Have
- Experience in healthcare, insurance, or regulated domains
- Exposure to AI governance, compliance, or risk frameworks
- Built internal AI platforms or reusable accelerators
- Prior experience tech‑leading enterprise AI initiatives
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
