Technical Lead - GenAI

Job Summary:

We are looking for a highly hands-on Back-End Engineer with a strong foundation in Python, system design, and practical experience in building and deploying Agentic Generative AI systems. The ideal candidate is passionate about solving complex problems using LLMs, understands the architecture of modern AI agent frameworks like LangChain/LangGraph, and can deliver scalable, cloud-native back-end services with a GenAI focus.

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.

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.