Data Scientist

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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