Design, prototype, and deliver agentic systems that plan and execute multi-step business tasks. Build automated workflows and connectors that integrate internal systems and APIs. Implement LLM-based agents (prompting, tool-calling, memory, monitoring) and harden prototypes for handoff. Produce code and deployable artifacts using AI-assisted generation. Collaborate with data analysts, data scientists, and data engineers as well as stakeholders to gain in-dept knowledge of business problems and likely solutions. Demo results and deliver concise handoff docs, runbooks, and impact metrics. What You'll Have:Master’s or PhD candidate or recent graduate in Data Science, Machine Learning, Computer Science, Applied Math, Statistics, Operations Research, or similar quantitative field. Demonstrable experience building AI prototypes or agentic projects (coursework, research, personal projects, or employment). Generate, test, and iterate code (examples: Python, JavaScript, YAML) using an IDE (VS Code, Jet. Brains, etc.) with AI-assisted coding (GitHub Copilot, OpenAI Codex,Claude Code, Cursor, or similar). Manual coding fluency is helpful but not mandatory. Familiarity with SQL and cloud data platforms (Databricks, Azure, AWS, or equivalent). Strong problem solving and communication skills; ability to present technical work to nontechnical stakeholders. Preferred / Nice-to-Have. Practical experience with model deployment and monitoring basics (packaging/deploying prototypes, logging/observability, basic cost and drift checks). Experience building or working with data/workflow pipelines or orchestration tools or familiarity with the concepts of scheduling and task orchestration. Experience working with unstructured text and retrieval approaches (RAG or index LLM patterns) for document/question answering. Comfortable integrating services via REST APIs and using secure authentication patterns.