Advanced AI Solutions Architect

3 days ago

Salem, Tamil Nadu, India beBeeGenai Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Title: Advanced AI Solutions Architect

Overview:

A seasoned Python professional with a strong foundation in backend engineering and recent production experience in Generative AI is sought after.

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.

This role is ideal for individuals who are passionate about architecting scalable AI solutions, writing production-grade code, and enabling 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 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.
  • Implement agentic workflows using Semantic Kernel, LangGraph, or similar orchestration tools.
  • Ensure system reliability, scalability, and security across GenAI-enabled services.

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.

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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