Chief GenAI Engineer

4 days ago

Noida, Uttar Pradesh, India beBeeGenerative Full time ₹ 2,00,00,000 - ₹ 2,50,00,000

Job Title: AI Architect

Experience: 8-12 Years

Job Description:

We are seeking a highly skilled Python Architect with expertise in backend engineering and production experience in Generative AI. The ideal candidate will have 8–10 years of core Python development experience, along with 2–2.5 years of focused engineering work in GenAI and microservices.

Key Responsibilities:

  • Design and implement Python-based microservices integrating GenAI capabilities
  • Develop GenAI-powered solutions using Retrieval-Augmented Generation (RAG) pipelines and LLM orchestration engines
  • Create RESTful APIs and backend services using FastAPI/Flask integrated with OpenAI/Azure OpenAI or open-source models
  • Work with DevOps engineers to enable CI/CD, monitoring, and observability for GenAI pipelines
  • Collaborate 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

Core Skills Required:

  • 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
  • Proven ability to design scalable, maintainable, and testable AI-driven applications

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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