Description

Key Responsibilities

-        Design, develop, and deploy agentic AI systems in Python using LangGraph and LangChain, including supervisor and sub-agent architectures, tool routing, and human-in-the-loop workflows.

-        Implement agent memory and context management strategies, including conversational state, long-term semantic memory, summarization, and context-window optimization.

-        Integrate AI agents with internal and third-party systems using the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.

-        Build and optimize RAG pipelines end to end, covering document ingestion, chunking, embedding, hybrid and semantic retrieval, re-ranking, and access-control-aware filtering.

-        Develop text-to-SQL and natural-language analytics capabilities over large relational schemas, including semantic catalogs, query validation, and execution guardrails.

-        Design and implement scalable backend services and microservices in FastAPI, including RESTful API design, request validation with Pydantic, dependency injection, authentication and authorization, API versioning, and rate limiting.

-        Build real-time streaming interfaces using WebSockets and Server-Sent Events to support token-level LLM response streaming and long-running agent executions.

-        Model, provision, and optimize application data stores, including relational databases such as PostgreSQL, vector databases for embedding storage and similarity search, document stores such as MongoDB, and caching layers such as Redis.

-        Own database lifecycle work including schema migrations, ORM usage, connection pooling, transaction management, and query performance tuning under production load.

-        Containerize applications with Docker and deploy to Kubernetes, managing autoscaling, resource allocation, secrets, and progressive rollout strategies.

-        Implement and maintain LLM gateway and routing infrastructure, including multi-provider failover, rate limiting, budget enforcement, and usage attribution.

-        Establish observability and evaluation practices for non-deterministic systems, including distributed tracing, structured logging, automated evaluations, and cost and latency monitoring.

-        Develop and maintain prompt engineering practices, including prompt versioning, regression testing, structured output enforcement, and mitigation of hallucination and prompt-injection risks.

-        Diagnose and resolve production incidents across the full stack, and contribute to runbooks, design documentation, and post-incident reviews.

-        Collaborate with product, data engineering, and business stakeholders to translate requirements into technical designs, and participate in architecture reviews.

-        Follow engineering best practices in code review, automated testing, version control, and CI/CD, and contribute to team technical standards.

Preferred Qualifications

-        Experience implementing MCP servers or clients, or A2A-based agent interoperability.

-        Familiarity with evaluation frameworks for agentic systems, such as LangSmith or Ragas.

-        Experience with workflow orchestration platforms such as Prefect, Temporal, Airflow, or Dagster.

-        Familiarity with enterprise identity and access management, including Okta, Microsoft Entra ID, SSO, SCIM, and OAuth 2.0.

-        Experience with document processing and ingestion at scale, including OCR, parsing of unstructured formats, and multimodal inputs.

-        Experience mentoring engineers or leading technical design for a delivery team.

Required/Desired Skills

Key Skills

Education

Not specified