Senior AI engineer

• Build and scale AI agent workflows using Python, LangGraph, LangChain, and CrewAI, with a focus on scalable agentic architectures.. • Design and maintain data, memory, and RAG layers using PostgreSQL, SQLAlchemy/Alembic, vector databases (Qdrant/pgvector), embeddings, and chunking.. • Develop LLM-powered applications and multi-agent workflows, including tool/function calling, orchestration, routing, and state management.. • Integrate enterprise platforms and data sources including Snowflake and Salesforce to support AI-driven business workflows.. • Build reliable FastAPI services with Pydantic, PyTest, Docker, and automated testing for production AI applications.. • Implement LLM evaluation, observability, guardrails, and prompt-injection protection to improve AI reliability, quality, and security.. • Deploy, scale, and operate AI/LLM services on GCP, including cloud-native and production deployment practices.. • Use GitHub/GitHub Actions for source control, CI/CD, code reviews, and automated deployment workflows.. • Tech Stack: Python 3.11+, Pydantic v2, PostgreSQL, SQLAlchemy/Alembic, LangGraph, LangChain, CrewAI, LLM Agents, RAG, Qdrant/pgvector, embeddings, FastAPI, PyTest, Docker, GCP, Snowflake, GitHub.. • Good to Have: Salesforce, Gong, Glean.. Show more Show less