Data Strategy Innovation Analyst

Merck Merck · Pharma · Telangana, India

This role focuses on designing, developing, and deploying Agentic AI systems for data governance, enablement, and stewardship within a healthcare company. The responsibilities include building AI agents capable of multi-step workflows, interacting with enterprise data, and reasoning over policies. The role also involves integrating these agents with existing systems, ensuring production-quality code, implementing guardrails for responsible AI, and monitoring deployed solutions.

What you'd actually do

  1. Design and implement Automation and Agentic AI systems to support data governance, enablement and stewardship activities. This individual will be responsible for designing the solution using approved architecture patterns, developing orchestration logic, tool use, and memory strategies. This will include developing last mile automation and AI capabilities to enable around data access management and automation
  2. Develop production‑quality code for basic automation, AI agents, services, and supporting infrastructure.
  3. Build agents capable of: Executing multi‑step workflows, Interacting with enterprise data, metadata, and knowledge systems, Reasoning over policies, standards, and governance rules, Escalating decisions or exceptions appropriately
  4. Integrate LLM‑based agents with existing data platforms, governance tools, catalogs, document repositories, and APIs.
  5. Apply modern software engineering best practices including modular design, version control, testing automation, observability, and CI/CD pipelines.

Skills

Required

  • Bachelor’s degree in computer science, Software Engineering, Data Science, or equivalent practical experience.
  • Strong hands‑on software engineering experience, with demonstrated delivery of production systems.
  • Experience developing LLM‑powered or AI‑driven applications, including orchestration, prompt engineering, and tool integration.
  • Proficiency in one or more modern programming languages (e.g., Python, TypeScript, Java).
  • Experience working with APIs, microservices, and cloud‑based architectures.
  • Familiarity with data management, metadata, data quality, governance, or knowledge systems.
  • Ability to move from ambiguous problem statements to implemented, running systems.

Nice to have

  • Direct experience building Agentic AI architectures using frameworks such as LangChain, Semantic Kernel, AutoGen, or similar.
  • Experience with enterprise data catalogs, governance platforms, or knowledge management systems.
  • Cloud deployment experience (e.g., Azure, AWS, or GCP), including security and identity integration.
  • Experience operationalizing AI: monitoring, cost control, reliability, and model lifecycle management.
  • Background in Responsible AI, compliance engineering, or AI risk mitigation.

What the JD emphasized

  • production systems
  • implemented, running systems
  • scalable, maintainable production services
  • company-wide platforms
  • data governance
  • privacy
  • security
  • Responsible AI principles
  • regulatory
  • security
  • internal governance requirements
  • Responsible AI controls

Other signals

  • design and implement Automation and Agentic AI systems
  • develop production-quality code for basic automation, AI agents, services
  • build agents capable of executing multi-step workflows
  • integrate LLM-based agents with existing data platforms
  • engineer guardrails to enforce data governance, privacy, security, and Responsible AI principles