Strategic AI Engineering Lead

4 days ago

Jamnagar, Gujarat, India beBeeGenAi Full time ₹ 1,50,00,000 - ₹ 1,80,00,000
Lead Gen AI Architect">

We are seeking a seasoned Python architect with 8–12 years of experience in backend engineering and production GenAI experience to architect scalable AI solutions, write production-grade code, and enable DevOps practices for AI-powered microservices in enterprise environments.

">Key Responsibilities:">
  • Design and implement Python-based microservices integrating GenAI capabilities.
  • Create GenAI-powered solutions using Retrieval-Augmented Generation (RAG) pipelines and LLM orchestration engines.
  • Develop RESTful APIs and backend services using FastAPI/Flask integrated with OpenAI/Azure OpenAI or open-source models.
  • Collaborate with DevOps engineers to enable CI/CD, monitoring, and observability for GenAI pipelines.
  • Work with ML engineers and data scientists to optimize GenAI system performance and scalability.
  • Lead design sessions, code reviews, and mentor junior engineers on architectural best practices.
  • Implement agentic workflows using Semantic Kernel, LangGraph, or similar orchestration tools.
">Core Skills:">
  • Expert-level Python development skills including experience with FastAPI, Flask, asyncio.
  • Strong understanding of microservices architecture and distributed systems.
  • Hands-on experience with GenAI tools: LangChain, LlamaIndex, Hugging Face Transformers.
  • Practical knowledge of deploying LLMs on cloud platforms (Azure OpenAI preferred).
  • Experience with REST API design and integration with external AI services.
  • Familiarity with vector databases like FAISS, Pinecone, or Weaviate.
  • DevOps practices: CI/CD with GitHub Actions, Docker, Kubernetes, Terraform.
">Secondary / Nice-to-Have Skills:">
  • Experience with agentic orchestration systems such as Autogen, Semantic Kernel, LangGraph.
  • Exposure to fine-tuning open-source LLMs and deployment in containerized environments.
  • Understanding of AI observability, performance tuning, and system logging.
  • Background in MLOps and collaboration with Data Science/ML teams.
  • Familiarity with agile development and cross-functional engineering collaboration.

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