AI Engineer

• Design and deploy production-ready Python services powering AI capabilities, leveraging FastAPI for robust API development.
• Architect and fine-tune end-to-end agent workflows supporting chat, search, and retrieval use cases.
• Develop intelligent agents using LangChain, LangGraph, and LangSmith, encompassing prompt engineering, integration, and comprehensive testing.
• Integrate and optimize vector databases (Qdrant, Milvus, etc.) for embeddings management, high-speed lookups, and hybrid search functionality.
• Continuously evaluate and monitor agent performance to ensure reliability, accuracy, and consistency in production environments.
• Optimize agent systems for reduced latency, cost efficiency, and horizontal scalability.

Required Qualifications

• 3+ years of total software engineering experience, with a strong foundation in backend development using Python.
• 1–2+ years of hands on experience deploying and maintaining ML/AI systems in production environments (beyond research or proof-of-concept stages).
• Demonstrated proficiency in Python and software engineering best practices, with a focus on clean, maintainable, and production-quality code.
• Solid experience with NLP, Retrieval Augmented Generation (RAG), embeddings, and vector databases such as Qdrant and Milvus.
• Deep expertise in LangChain and/or LlamaIndex for building agentic AI systems.
• Strong experience with Langsmith, understanding Evaluation Benchmarking and Tracing
• Proven experience in prompt engineering and systematic agent testing methodologies.
• Strong understanding of LLM inference pipelines, both on local infrastructure and cloud platforms.
• Proficiency with Docker and cloud platforms (AWS, GCP, or Azure).
  • Demonstrated ability to design clean, well-documented APIs and integrate seamlessly with existing backend systems using a microservices architecture.