Head Platform Engineer - Machine Learning

Responsibilities :

- Build and scale ML engineering and data engineering functions.

- Establish MLOps frameworks for standardised, production-grade model development and monitoring.

- Ensure smooth model transition from data science experimentation to live deployment.

- Design and operationalise a centralised decisioning platform that integrates low-code model development, AutoML, rule engines, and workflow automation.

- Enable DS and Risk teams to build, test, and deploy models with minimal engineering bottlenecks.

- Expand decisioning systems across functional podscredit, pricing, collections, fraud, cross-sell, and customer management drive consistent, explainable, and auditable decision-making.

- Ensure the platform is scalable, modular, and compliant with RBI regulations.

- Build modern, scalable data platforms (real-time ingestion, lakehouse, event-driven systems).

- Ensure full lifecycle governance of data from sourcing to archival.

- Partner with governance teams to enable lineage, auditability, and regulatory compliance.

- Lead DataOps, L1/L2 support, and SRE teams to maintain > 99.5% platform uptime.

- Implement automated testing, proactive monitoring, and self-healing systems.

- Optimise infra utilisation and cloud cost efficiency.

- Act as execution partner to the Head of Product and Strategy and functional leaders.

- Deliver platform capabilities and decisioning products aligned to business KPIs (loan volume growth, risk reduction, ticket size expansion, collections efficiency).

- Manage technology partnerships and vendor ecosystems (e. g., Databricks, automation tools).

Requirements :

- 15 ~ 20 years of experience in data engineering, ML engineering, or platform leadership, with at least 8 ~10 years in senior management roles.

- Proven success in building and scaling large-scale data/ML platforms in fast-paced environments (fintech preferred).

- Strong academic foundation with Bachelor's/Master's/PhD in Computer Science, Engineering, or quantitative fields from top-tier Indian institutions (IIT/IISc/BITS/NIT).

- Deep expertise in data platform design (streaming, lakehouse, event-driven, real-time ingestion).

- Hands-on knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI).

- Strong background in model lifecycle managementdeployment, monitoring, retraining, and governance.

- Experience operationalising decisioning platforms combining rules, ML, AutoML, and workflow automation.

- Expertise in distributed computing and big data frameworks (Spark, Hadoop, Kafka, Flink).

- Proficiency in cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).

- Strong understanding of data governance, lineage, and compliance frameworks in regulated industries (RBI, GDPR).

- Solid programming and scripting experience (Python, SQL, Scala/Java) with knowledge of ML/DL libraries (TensorFlow, PyTorch, Scikit-learn).

- Track record of driving platform reliability, resilience, and performance through DataOps and SRE practices.

- Ability to manage and optimise infra utilisation and cloud costs at scale.

- Excellent leadership skills with experience managing 15+ member teams across engineering and platform functions.

- Strong communication, stakeholder management, and vendor negotiation skills to bridge business and technology.