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Mission
The GenAI Data Scientist designs, builds, evaluates, and deploys Generative AI solutions, with strong focus on GenAI agents and agentic workflows.
He/she Is Responsible For
- Understanding business needs and converting them into GenAI agent use cases
- Building GenAI agents using LangGraph or similar agentic frameworks
- Designing workflows with tool calling, routing, memory, state management, guardrails, and human escalation
- Connecting agents with enterprise data, APIs, databases, applications, and automation tools
- Building and optimizing RAG knowledge stores when knowledge retrieval is required
- Developing clean, modular, scalable, and deployment-ready Python code
- Evaluating agents for accuracy, reliability, hallucination risk, latency, cost, and user experience
- Working with data, platform, and software engineering teams to move GenAI solutions to production
Key Expected Achievements
- Business need is translated into a clear GenAI agent solution
- Agent architecture, workflow, tools, memory, prompts, and guardrails are designed and documented
- Agentic workflows are built, tested, and optimized using LangGraph or similar frameworks
- RAG knowledge stores are implemented and optimized when required
- Python code is modular, testable, maintainable, and production-ready
- The solution is deployed with logging, monitoring, error handling, access control, and cost control
- Results, limitations, risks, and usage guidelines are clearly presented to business and technical stakeholders
- Source code, prompts, configuration, and documentation are delivered
- Peer reviews are organized to ensure quality, scalability, and reliability
