28 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise standards and reference patterns for semantic models, dimensional models, business metrics, KPIs, hierarchies, calculations, and reusable analytical assets.
- Architect semantic consumption patterns across Microsoft Fabric, Power BI, Databricks Gold-layer data, and approved AI applications.
- Design engineering standards and reusable patterns for deploying, versioning, testing, monitoring, and governing machine learning and generative AI solutions.
- Guide feature pipelines, model registries, inference workflows, retrieval pipelines, vectorized knowledge assets, and AI evaluation frameworks into secure production services.
- Design curated, governed data products and knowledge assets for predictive analytics, generative AI, copilots, agents, and retrieval-augmented generation.
- Ensure semantic and AI assets include appropriate metadata, lineage, ownership, access policies, traceability, reproducibility, and change controls.
- Serve as the technical escalation point for complex semantic, analytics, MLOps, and AI engineering decisions.
- Create reference implementations, reusable frameworks, coding standards, and engineering playbooks.
- Mentor Senior Analytics & AI Engineers, Semantic Engineers, Data Analysts, and Data Scientists.
- Lead architecture reviews, influence roadmaps, communicate technical trade-offs and risks, and evaluate emerging technologies.
Requirements
- 10+ years of experience across analytics engineering, business intelligence, semantic modeling, data engineering, machine learning engineering, MLOps, or related disciplines.
- Principal-level technical leadership or architecture experience for enterprise data, analytics, or AI solutions.
- Deep expertise in SQL, dimensional modeling, semantic model design, analytical performance optimization, and governed metric implementation.
- Hands-on experience with Microsoft Power BI and/or Microsoft Fabric semantic models, including DAX and enterprise-scale model design.
- Hands-on experience with Databricks or a comparable cloud data and AI platform, including production data pipelines and model lifecycle capabilities.
- Experience designing or operating experiment tracking, model registries, monitoring, evaluation, observability, and deployment workflows for ML or generative AI solutions.
- Working knowledge of embeddings, vector retrieval, retrieval-augmented generation, prompt and model evaluation, and agent-based solutions.
- Strong understanding of metadata, lineage, data quality, access control, business glossaries, and responsible AI principles.
- Ability to mentor technical professionals, lead architecture reviews, and influence stakeholders without direct authority.
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field; equivalent practical experience is considered.
- Preferred experience with Microsoft Purview, Unity Catalog, MLflow, Azure DevOps or GitHub Actions, cloud observability tooling, enterprise analytics domains, and federated semantic or AI platform capabilities.
Tech Stack
Categories
Data EngineeringML Engineering
About Commvault
Commvault builds enterprise data protection and cyber resilience software that secures, backs up, and recovers workloads across on‑prem, cloud, and SaaS environments. It sells subscriptions and SaaS options, plus professional services, to organizations needing backup, disaster recovery, and ransomware recovery. Founded in 1996 and headquartered in Tinton Falls, NJ, Commvault is a public company traded on NASDAQ under the symbol CVLT.