Gen AI Programmer

Job Summary:

highly hands-on Back-End Engineer

Key Responsibilities:

• Design and implement robust, scalable back-end systems for

  GenAI agent-based platforms

  .
• Work closely with AI researchers and front-end teams to integrate

  LLMs and agentic workflows

  into production services.
• Develop and maintain services using Python (FastAPI/Django/Flask), with best practices in modularity and performance.
• Leverage and extend frameworks like

  LangChain, LangGraph

  , and similar to orchestrate tool-augmented AI agents.
• Design and deploy systems in

  Azure Cloud

  , including usage of serverless functions, Kubernetes, and scalable data services.
• Build and maintain

  event-driven / streaming architectures

  using Kafka, Event Hubs, or other messaging frameworks.
• Implement inter-service communication using

  gRPC

  and REST.
• Contribute to architectural discussions, especially around distributed systems, data flow, and fault tolerance.
• Optionally integrate and support legacy or industry-specific protocols like

  MCP and A2A

  .
• Python, FastAPI , distributed scalable microservices experience.

Required Skills & Qualifications:

• 5+ years of hands-on back-end development experience in

  Python

  .
• Strong track record on platforms like

  LeetCode

  or in real-world algorithmic/system problem-solving.
• Deep knowledge of at least one

  Python web framework

  (e.g., FastAPI, Flask, Django).
• Solid understanding of

  LangChain, LangGraph, or equivalent LLM agent orchestration tools

  .
• 2+ years of hands-on experience in

  Generative AI systems and LLM-based platforms

  .
• Proven experience with

  system architecture

  , distributed systems, and microservices.
• Strong familiarity with

  Any Cloud

  infrastructure and deployment practices.
• Practical experience with

  event-driven architectures

  and

  streaming systems

  .
• Familiarity with

  gRPC

  , asynchronous programming, and modern API design.

Nice to Have (Preferred):

• Experience with

  MCP (model context protocol)

  or

  A2A (Application-to-Application)

  integrations.
• Contributions to open-source projects or personal repos showcasing GenAI / agentic implementations.
• Understanding of MLOps and CI/CD practices for AI-driven systems.
• Prior experience with vector databases, RAG pipelines, or fine-tuning LLMs.