Staff AI Development Engineer (Enterprise AI Ecosystem)
1. Advanced Agentic Architecture Custom Workflow Design: Architect and implement complex, cyclic AI workflows using LangGraph. Design state machines that enable agents to plan, execute, reflect, and retry tasks autonomously. State Management: Engineer robust persistence layers (checkpointers) to manage long-running agent conversations, enabling "human-in-the-loop" interactions where users can guide or correct agent behavior mid-flight. Multi-Agent Orchestration: Build custom orchestration layers where specialized agents (e.g., "Code Reviewer," "Doc Searcher," "Test Generator") collaborate via defined graph edges and conditional routing logic. 2.Framework & Tool Development Tool Abstraction: Develop standardized interfaces (based on LangChain tools) that allow agents to securely interact with enterprise systems (Jira, GitLab, SQL Databases, Proprietary APIs). Graph-Based RAG: Go beyond simple semantic search by implementing GraphRAG and advanced retrieval strategies. Build pipelines that combine vector search with knowledge graph traversals to answer complex engineering queries. 3.Production Engineering & Scalability Asynchronous Python: Write high-performance, non-blocking Python code using asyncio to handle concurrent agent execution and high-throughput token streaming. API Design: Expose agent workflows via FastAPI microservices, ensuring strict typing (Pydantic), validation, and comprehensive open-api documentation. Evaluation & Observability: Design "LLM-as-a-Judge" evaluation pipelines to measure the efficacy of custom agent graphs. Implement tracing (e.g., LangSmith or internal equivalents) to debug complex chains of thought. Pattern Definition: Define and document design patterns for agentic AI (e.g., Plan-and-Solve, ReAct, Map-Reduce) for the wider engineering organization. Mentorship: Serve as a technical mentor to Senior and Junior engineers, conducting rigorous code reviews and driving best practices in Python software design and AI safety. 7+ years of professional Software Engineering experience. Python Mastery: Expert-level proficiency in Python. Deep understanding of AsyncIO, Pydantic, decorators, and metaprogramming. LangChain/LangGraph: Proven, hands-on experience building applications with LangChain and LangGraph. You must understand the difference between a Chain (DAG) and a Graph (Cyclic) and know when to apply each. AI Fundamentals: Solid understanding of LLM mechanics (context windows, tokenization, temperature), embedding models, and vector stores. System Design: Experience architecting distributed systems, microservices, and handling state in stateless environments (REST). Master's or PhD in Computer Science or Artificial Intelligence. Advanced RAG: Experience implementing Hybrid Search (Keyword + Semantic), Re-ranking (Cross-Encoders), or Parent-Child document retrieval strategies. Graph Theory: Academic or practical understanding of graph theory, finite state machines (FSM), and their application to AI control flow. Database Expertise: Hands-on experience with Vector Databases (Milvus, Qdrant, Chroma) and Graph Databases (Neo4j). DevOps/MLOps: Familiarity with Docker, Kubernetes, and CI/CD pipelines for deploying AI applications. Evaluation Frameworks: Experience with frameworks like Ragas or DeepEval for quantifying RAG and Agent performance. Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
