AI Systems Architect

1 day ago

Anand, Gujarat, India beBeeGenerative Full time ₹ 18,00,000 - ₹ 21,00,000
Job Description

We are seeking an experienced Python Architect to lead the development of our Generative AI solutions. This role requires a strong foundation in backend engineering and recent production experience in GenAI.

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 individual will be responsible for architecting and implementing Python-based microservices integrating GenAI capabilities.

Responsibilities
  • Design and implement 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
Required Skills and QualificationsCore 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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