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Principal Product AI Data EngineerClarivate · IND - Bangalore (DRG)
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Clarivate

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Principal Product AI Data Engineer

🗓️ Posted 2026-09-02 IND - Bangalore (DRG) Full-time Hybrid ICT
💰 Salary Not shared 📊 Market 2,500,000 GBP–3,800,000 GBP/mo

About the Company

We are seeking a Principal Product AI Data Engineer to join our MedTech team in Bangalore. In this role, you will lead the design and delivery of AI-enabled data products and intelligent workflows that drive innovation in the Life Sciences and Healthcare domain.

Responsibilities

  • ● s to build scalable, high-quality solutions that deliver measurable business impact.

Requirements

  • ● Bachelor's Degree or equivalent in Computer Science, Software Engineering, Artificial Intelligence, or related field
  • ● + years of experience building and delivering production-grade software, including deployment of AI-powered product capabilities
  • ● Strong experience with Python, PySpark, Snowflake, Databricks, Airflow, Delta Lake, and cloud platforms such as AWS or Azure
  • ● Strong SQL and database expertise across platforms such as PostgreSQL, Oracle, Snowflake, and Databricks
  • ● Strong verbal and written communication skills
  • ● It Would Be Great If You Also Had
  • ● Understanding of healthcare data and regulations
  • ● What Will You Be Doing in This Role
  • ● Lead the design, architecture, and delivery of complex Generative and Agentic AI systems across teams and platforms
  • ● Execute high-impact AI initiatives ensuring solutions are scalable, reliable, and aligned with business goals
  • ● Establish engineering standards and guide implementation best practices
  • ● Drive cross-functional collaboration and mentor engineers to foster technical growth
  • ● Design innovative AI solutions including data pipelines, architectures, and agentic workflows
  • ● Partner with product, design, and business leaders to align AI capabilities with strategic priorities
  • ● AI/ML Engineering Responsibilities
  • ● Collaborate with Data Scientists to productionize machine learning models
  • ● Design and implement feature engineering pipelines
  • ● Develop scalable ML data pipelines and model-serving architectures
  • ● Support MLOps practices, including model deployment, monitoring, retraining, and governance
  • ● Integrate Generative AI, LLMs, and AI agents into enterprise applications