ROLE OVERVIEW :

The Head of Data, ML &

- AI Platform Engineering will serve as the executive technical leader responsible for building, scaling, and operationalizing next-generation data infrastructure, machine learning execution engines, and enterprise AI platform capabilities.

This high-impact role brings together Data Engineering, ML Engineering, MLOps, LLMOps/Agentic AI Infrastructure, Enterprise Decisioning Platforms, and SRE/DataOps into a cohesive, production-grade engineering organization.

Key Responsibilities :

- Engineering Leadership : Build, scale, and mentor Data Engineering, ML Engineering, MLOps, and AI Platform engineering teams while establishing engineering best practices and operational rigor across squads.
- Enterprise Decisioning Platform : Design, operationalize, and scale a centralized decisioning platform integrating low-code model development, AutoML, real-time rule engines, policy-as-code, real-time scoring, and automated workflow execution.
- Data Platform Architecture : Lead the strategic vision and implementation of scalable, cloud-native Lakehouse architectures using technologies such as Databricks, Delta Lake, Unity Catalog, DLT, and Spark for high-volume batch and real-time streaming workloads.
- MLOps &
- LLMOps Standardization : Establish standardized,



production-grade MLOps frameworks along with scalable LLM and Agentic AI infrastructure.
- Production Deployment &
- Scaling : Partner closely with Data Science teams to enable seamless and automated transition of machine learning and deep learning models from experimentation to highly available production environments.
- Operational Excellence &
- SRE : Lead DataOps and SRE functions to ensure high platform availability, proactive automated testing, self-healing systems, and continuous CI/CD delivery.
- Model &
- Data Governance : Operationalize comprehensive model lifecycle governance and regulatory compliance frameworks aligned with applicable financial-services regulations, data-protection requirements, and internal risk policies.
- Observability &
- System Health : Establish end-to-end telemetry and monitoring.

Requirements :

- 15 - 20 years of total experience
- 8 years in senior management - platform leadership
- Only from Top-tier education - IIT / IISc / BITS / NIT / IIIT
- Robust Data Engineering &
- Data Platform Architecture
- Deep MLOps - ML Platform experience
- AI/LLMOps - Agentic AI infrastructure
- Cloud &
- Infrastructure - AWS/GCP, Kubernetes, Docker, IaC
- Large-scale engineering leadership
- Fintech/NBFC/Banking lending/credit/risk/fraud exposure
- Real-time decisioning production-grade platform experience

📌 Data/ML & AI Platform Engineering Head (India)
🏢 Neemtree
📍 India