Description

Responsibilities:

• Build end-to-end architecture for an enterprise agentic AI platform — multi-agent orchestration, tool/MCP ecosystems, RAG, evaluation, and production guardrails. Define the reference architecture and lead technical decisions across squads.
• Design multi-agent systems (planner/executor/critic, supervisor-worker, swarm patterns) using frameworks such as Claude Agent SDK, Lang Graph
• Define the MCP (Model Context Protocol) tool catalog, schema, server topology, authn/authz model, and governance for tool exposure across agents.
• Architect hybrid RAG (vector + lexical/BM25, e.g., pgvector + Postgres) with chunking, re-ranking, citation, and freshness strategies.
• Establish patterns for memory (short/long-term, episodic), state persistence, human-in-the-loop gates, and deterministic checkpoints.
• Define model routing (Opus/Sonnet/Haiku), cost/latency budgets, and fallback strategies.
• Set evaluation strategy: golden sets, LLM-as-judge, trajectory eval, regression suites, and observability (LangSmith/Lang Fuse/CloudWatch).
• Drive security/compliance: prompt-injection defenses, secret hygiene, PII handling, data residency, model risk management.
• Author ADRs, HLDs, and reference implementations; mentor developers and review code.

Requirements:

• This role requires deep expertise in Agentic AI, MCP, and related emerging technologies.
• Hands-on with at least two agentic frameworks (Claude Agent SDK, Lang Graph, AutoGen).
• Deep LLM application engineering: tool/function calling, structured outputs, prompt engineering, context window management, caching.
• Strong cloud architecture on AWS (Bedrock, Lambda, ECS/EKS, S3, SQS/Event Bridge).
• RAG at scale: pgvector / OpenSearch / Pinecone, embedding model selection, hybrid retrieval, re-ranking.
• MCP server/client design and tool integration patterns

Key Skills

Education

Any Graduate