Mandatory skill:
- 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
- Solid 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
- 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
Description:
Python (strong proficiency)
- LangChain & LangGraph
- RAG (Retrieval-Augmented Generation)
- Multi-agent systems
- Memory systems
📌 Gen AI with python (Chennai)
🏢 Sourcedear
📍 Chennai
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