Gen AI / Python Developer

Responsibilities:

• Design, build, and enhance AI and generative AI solutions using Python.
• Develop and manage agentic workflows for complex task execution.
• Create effective prompts, system instructions, and agent interaction patterns.
• Build AI applications using Python-based tools, frameworks, and ADKs.
• Work with MCP, agent/tool integration patterns, and orchestration frameworks.
• Implement and manage RAG-based techniques for context-aware AI use cases.
• Use MLflow or similar tools to support experiment tracking and model lifecycle workflows.
• Collaborate in an Agile delivery environment with cross-functional teams.
• Translate business requirements into scalable AI-enabled solutions.
• Support testing, debugging, and optimization of Python-based AI applications.

Requirements:

• Experience in Python (strong proficiency), LangChain and LangGraph, RAG (Retrieval-Augmented Generation), multi-agent systems, and memory systems (long-term/short-term, persistence, and conversational checkpoints).
• Strong hands-on experience in Python development.
• Strong understanding of AI and generative AI concepts.
• Experience building agentic AI solutions.
• Hands-on experience with Python ADK or similar agent development frameworks.
• Good understanding of prompt engineering.
• Ability to write effective instructions for agents and define agent behaviors.
• Experience with agentic flows / orchestration patterns.
• Working knowledge of MCP (Model Context Protocol).
• Experience with RAG techniques.
• Familiarity with MLflow or similar ML lifecycle tooling.
• Exposure to LangGraph or graph-based orchestration frameworks.
• Good understanding of Agile / Agile team ways of working.
• Strong debugging, problem-solving, and Python coding skills.

Good to have:

• Experience in big data ecosystems.
• Hands-on experience with PySpark.
• A complementary secondary skill aligned to the primary AI/Python skillset, such as data engineering, cloud, or MLOps.
• Exposure to scalable data processing pipelines.
• Familiarity with deploying AI workloads in enterprise environments.