Lead Generative AI Engineer

Lead Generative AI Engineer

Build and scale a production multi-agent AI platform serving thousands of internal users across multiple business units. Monthly release cadence, real users, real latency, real cost.

Skills:

Python, Machine Learning, Artificial Intelligence(AI)~Generative AI

Experience

Required:

6 - 10 years relevant experience

Must Have:

• Strong hands-on experience in building Agentic AI solutions and frameworks.
• Associates should demonstrate their experience designing and implementing Agentic AI solutions in real-world environments.
• A self-driven mindset with the ability to work independently and take ownership.
• Experience working on large-scale initiatives for major enterprise clients.
• Clear articulation of their use cases, specific contributions and impact within project teams.
• Ability to confidently explain solution scenarios, architecture decisions, challenges, and outcomes.
• Confidence and capability to design and develop their own AI use cases from concept to execution.

Responsibilities:

• LLM-driven orchestrator that routes user intent across a portfolio of specialized agents — delegation, memory, response validation, capability discovery.
• Agent selection layer — hybrid retrieval (vector RAG over a capability registry) plus closed-set LLM selection with JSON-schema-constrained outputs.
• Multi-agent SDK / gateway — FastAPI service hosting many agents behind path-prefix routing, per-agent tool registries, session-scoped conversational context.
• Tool-driven agents — 15–30 tools per agent composed dynamically by an LLM; owns tool contracts, guardrails, and evaluation.
• Data API layer — parameterized endpoints between agents and databases; LLMs never touch DBs directly.
• Partner-team onboarding — versioned A2A contract, bring-your-own-agent registration, auto re-embedding.

Core AI Engineering:

• Production LLM systems: RAG, tool/function-calling loops, structured outputs, hallucination guards, closed-set selection.
• Multi-agent orchestration: A2A protocols, session affinity, human-in-the-loop gating, kill switches, graceful degradation.
• Vector search + embeddings at scale (sub-second retrieval over thousands of docs).
• Evaluation & safety: PII/PHI masking, audit trails, feedback-loop instrumentation, offline + online eval.

Platform / Infrastructure:

• Python 3.11+, FastAPI, async I/O, Pydantic.
• Modern LLM stacks (Gemini, GPT, Claude) and agent frameworks (Lang Graph, Agent SDKs).
• Cloud (GCP or AWS):
  Kubernetes, object storage, workflow orchestration, Vertex/Bedrock-class services.
• Redis, MongoDB, Oracle/Postgres, SSO + RBAC.
• Observability:
  Prometheus, structured JSON logs, per-decision audit trails, p95 latency SLOs in seconds.

Company Benefits & Culture:

• Inclusive and diverse work environment.
• Opportunities for professional growth and development.
• Comprehensive health and wellness benefits.