Description

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 (LangGraph, 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.

Skills: Digital :

 Python~Digital : Machine Learning~Digital : Artificial Intelligence(AI)~Generative AI Experience Required: 10 & Above

Key Skills

Education

Not specified