Chief AI Engineering Leader

19 hours ago

Kannur, Kerala, India beBeePythongenerativeai Full time ₹ 1,80,00,000 - ₹ 2,00,00,000

Job Title: Python Architect

About the Role:

We are seeking a skilled Python Architect with a strong foundation in backend engineering and recent production experience in Generative AI.

The ideal candidate will have extensive core Python development experience, along with focused engineering work in GenAI and microservices.

Primary Responsibilities:

  • Design and implement scalable GenAI 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
  • Ensure system reliability, scalability, and security across GenAI-enabled services

Required Skills and Qualifications:

  • 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
  • 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

Bonus Points:

  • 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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