compLogoGenerative AI EngineerCompany: Incedo IncHybridBasking Ridge, Bernards, NJ, USA
Role: Generative AI Engineer
Location: Basking Ridge NJ
Experience: 6+ years
Work Mode: Hybrid (3 days WFO)

Core Responsibilities
  • Architect Agentic Systems: Design and deploy stateful agents using LangGraph and LangChain, focusing on long-running workflows with unified PostgreSQL checkpointers for persistent state management.
  • Develop High-Performance APIs: Build robust backends using Python (Asyncio), FastAPI, and Pydantic to handle high-concurrency AI workloads.
  • Optimize Retrieval (RAG): Implement advanced RAG pipelines using Elasticsearch (Vector Search), cross-encoders for re-ranking, and custom embedding services.
  • Infrastructure & Deployment: Deploy containerized AI services on Google Cloud Platform (GCP), integrating seamlessly with Google Vertex AI.
  • Engineering Excellence: Adapt and contribute to internal SDKs that extend open-source frameworks to provide enterprise-grade observability, model routing, and state persistence.
  • Frontend Integration: Build intuitive UIs in React.js to allow users to interact with complex agentic outputs and FastAPI backends.

Technical Requirements
Python & Backend Excellence
  • Expertise in Object-Oriented Programming (OOP) and asynchronous patterns (async/await).
  • Deep experience with FastAPI and data validation using Pydantic models.
GenAI & Agentic Frameworks
  • LangChain/LangGraph: Proven track record of building stateful agents.
  • Protocol Knowledge: Familiarity with Agent-to-Agent (A2A) protocols for multi-agent coordination and Model Context Protocol (MCP) for building dedicated tool servers.
  • Observability: Experience using frameworks like Galileo for AI evaluation and monitoring.
Data & Search Layer
  • PostgreSQL: Proficiency in managing task coordination, state storage, and unified connection pooling.
  • Elasticsearch: Practical knowledge of document indexing, Vector DBs, and retrieval strategies (Similarity search, Hybrid search).
Cloud & DevOps
  • Hands-on experience with GCP, specifically deploying containerized services (Cloud Run/GKE).
  • Integration experience with Vertex AI model ecosystems.