Senior AI Systems Architect

2 days ago

Palakkad, Kerala, India beBeePython Full time ₹ 20,00,000 - ₹ 30,00,000
Job Description:

We are seeking a seasoned Python architect to spearhead the development of scalable AI solutions. The ideal candidate will possess 8-10 years of core Python development experience, along with 2-2.5 years of focused engineering work in GenAI and microservices.

The successful candidate will have hands-on experience with FastAPI/Flask integrated with OpenAI/Azure OpenAI or open-source models. They will also be well-versed in RESTful APIs, backend services, and DevOps practices for AI-powered microservices in enterprise environments.

Key Responsibilities:

  • Architect and implement Python-based microservices integrating GenAI capabilities
  • Design GenAI-powered solutions using Retrieval-Augmented Generation (RAG) pipelines and LLM orchestration engines
  • Develop RESTful APIs and backend services
  • 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
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
Core Skills Required:

The ideal candidate will have a strong background in backend engineering, recent production experience in Generative AI, and a proven track record of designing scalable, maintainable, and testable AI-driven applications.

Nice-to-Have Skills:

The following skills are highly desirable but not mandatory:

  • 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

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