Data Strategy Innovation Analyst

Merck Merck · Pharma · Telangana, India

The Data Strategy Innovation Analyst role focuses on designing, developing, and deploying Automation and Agentic AI systems for data governance, enablement, and stewardship within a healthcare biopharma company. This involves building LLM-powered agents capable of multi-step workflows, interacting with enterprise systems, reasoning over policies, and integrating with various data platforms. The role emphasizes production-quality code, software engineering best practices, deployment, monitoring, scaling, and ensuring compliance with data governance, privacy, security, and Responsible AI principles. Experience with agentic AI architectures and operationalizing AI is preferred.

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

  • Python
  • TypeScript
  • Java
  • APIs
  • microservices
  • cloud-based architectures
  • data management
  • metadata
  • data quality
  • governance
  • knowledge systems
  • software engineering

Nice to have

  • LangChain
  • Semantic Kernel
  • AutoGen
  • enterprise data catalogs
  • governance platforms
  • knowledge management systems
  • Azure
  • AWS
  • GCP
  • Responsible AI
  • compliance engineering
  • AI risk mitigation

What the JD emphasized

  • production systems
  • LLM-powered or AI-driven applications
  • implemented, running systems
  • Agentic AI architectures
  • scalable, maintainable production services
  • company-wide platforms

Other signals

  • design and implement Automation and Agentic AI systems
  • develop production-quality code for basic automation, AI agents, services, and supporting infrastructure
  • Build agents capable of executing multi-step workflows, interacting with enterprise data, metadata, and knowledge systems, reasoning over policies, standards, and governance rules, and escalating decisions or exceptions appropriately
  • Integrate LLM-based agents with existing data platforms, governance tools, catalogs, document repositories, and APIs
  • Apply modern software engineering best practices including modular design, version control, testing automation, observability, and CI/CD pipelines
  • Package and deploy AI solutions into development, test, and production environments
  • Monitor agent behavior, performance, and outputs to ensure reliability, traceability, and policy compliance
  • Diagnose and remediate failures, hallucinations, workflow breaks, or data quality dependencies
  • Refactor prototypes into scalable, maintainable production services
  • Support the expansion of successful agents from team-level solutions to company-wide platforms
  • Engineer guardrails to enforce data governance, privacy, security, and Responsible AI principles
  • Implement logging, auditing, explainability, and versioning for AI agents and prompts
  • Ensure solutions comply with regulatory, security, and internal governance requirements
  • Collaborate with governance and legal partners to operationalize Responsible AI controls in code and architecture
  • Develop agents that improve knowledge capture, classification, retrieval, and reuse
  • Produce technical documentation, architecture diagrams, and runbooks for AI solutions
  • Enable other teams to adopt, extend, or integrate AI agents through reusable patterns and components
  • Strong hands-on software engineering experience, with demonstrated delivery of production systems
  • 3 Years +Experience developing LLM-powered or AI-driven applications, including orchestration, prompt engineering, and tool integration
  • Ability to move from ambiguous problem statements to implemented, running systems