Python/Gen AI Lead Developre

Contribute to the establishment of best practices, methodologies, and a culture of innovation within the team. • Present architectural decisions, technical findings, and recommendations to both technical and business audiences. Role & responsibilities

• Extensive experience (5-10 years) in backend architecture and design for scalable, distributed systems using Python and modern frameworks (FastAPI, Django REST Framework, Flask).

• Proficiency in building Asynchronous code. Must have a good knowledge of a web framework like FastAPI, DRF, or Flask, with specific, hands-on experience using asyncio to build scalable, I/O-bound services.
• Proven expertise in architectural decision-making, evaluating trade-offs, and designing robust, maintainable solutions for complex business problems.
• Strong background in code review, enforcing best practices, mentoring team members, and driving continuous improvement in code quality and maintainability.
• Deep experience in Multi-Agent System workflow and design, including orchestration, communication protocols, and agent lifecycle management using frameworks such as LangChain, AutoGen, CrewAI, or similar.
• Advanced skills in database schema design and optimization, including relational (PostgreSQL, MySQL) and NoSQL databases, with a focus on scalability, normalization, and performance.
• Expertise in API design, including RESTful, asynchronous, and event-driven APIs, ensuring security, scalability, and maintainability.
• Experience in breaking down complex requirements into actionable tasks, creating detailed work breakdown structures, and aligning deliverables with business objectives.

• Strong skills in effort estimation, resource planning, and risk assessment, ensuring timely and predictable delivery of high-quality solutions.

• Strong proficiency with Python testing frameworks like pytest, with a focus on writing comprehensive unit, functional, and integration tests.
• Solid understanding of Python packaging, dependency management, and virtual environments, with hands-on experience using tools like Poetry, uv, pip, and virtualenv/venv.
• Strong understanding of basics of SQL reading and writing SQL queries, a basic understanding of database interaction tools schema design, and database optimization.
• Hands-on experience with Python data libraries (Pandas, NumPy)
• Good knowledge of API development and testing including but not limited to HTTP, RESTful services, Postman, and allied cloud-based services like API Gateway.
• Should have a keen eye for architecture. Understand the trade-off between architectural choices, both on a theoretical level and an applied level.
• Good exposure of LLM SDKs(e.g., OpenAI, Anthropic, Azure OpenAI, Google Gemini).
• Understanding of LLM orchestration and lifecycle management, including prompt engineering, agent state management, and debugging agentic loops.
• Familiarity with Retrieval-Augmented Generation (RAG) patterns and practical experience with vector databases (e.g., Pinecone, Weaviate, ChromaDB, or pgvector) for managing long-term memory and knowledge bases for agents.
• Strong grasp of Agentic AI concepts, including the ability to design, build, and orchestrate autonomous agents that can reason, plan, and execute tasks using a predefined set of tools.
• Experience with multi-agent systems and frameworks (LangChain, AutoGen, Google ADK, CrewAI) or building complex chains and agentic workflows.
• Familiarity with emerging open standards for AI interoperability, including the Model Context Protocol (MCP) for secure agent-tool communication and the Agent2Agent (A2A) protocol for multi-agent collaboration.
• Strong understanding on at-least one cloud platform (AWS, GCP, Azure) to deploy, manage, and scale applications.
• Strong proficiency with Git for version control, including hands-on experience with collaborative workflows on platforms like GitHub or Bitbucket (e.g., branching, pull/merge requests, and code reviews).
• Experience in presenting Proof of Concepts (POC) findings, including performance benchmarks, potential risks, and strategic recommendations to both technical and non-technical stakeholders. Proven ability to translate successful POCs into wellarchitected, scalable, and production-ready solutions.
• Good to have hands-on experience with AI coding assistants like GitHub Copilot and familiarity with agent development platforms such as Google’s Agentspace or similar tools

Preferred candidate profile

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