Head of Machine Learning Infrastructure

Job description

Established in 2019, Credit Saison India (CS India) is one of the country’s fastest growing Non-Bank Financial Company (NBFC) lenders, with verticals in wholesale, direct lending and tech-enabled partnerships with Non-Bank Financial Companies (NBFCs) and fintechs.

Its tech-enabled model coupled with underwriting capability facilitates lending at scale, meeting India’s huge gap for credit, especially with underserved and under penetrated segments of the population.

Credit Saison India is committed to growing as a lender and evolving its offerings in India for the long-term for MSMEs, households, individuals and more.

Credit Saison India is registered with the Reserve Bank of India (RBI) and has an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings.

Currently, Credit Saison India has a branch network of 80+ physical offices, 2.06 million active loans, an AUM of over US$2B and an employee base of about 1,400 employees.

Credit Saison India is part of Saison International, a global financial company with a mission to bring people, partners and technology together, creating resilient and innovative financial solutions for positive impact.

Across its business arms of lending and corporate venture capital, Saison International is committed to being a transformative partner in creating opportunities and enabling the dreams of people.

Saison International is the international headquarters (IHQ) of Credit Saison Company Limited, founded in 1951 and one of Japan’s largest lending conglomerates with over 70 years of history and listed on the Tokyo Stock Exchange.

The Company has evolved from a credit-card issuer to a diversified financial services provider across payments, leasing, finance, real estate and entertainment.

Based in Singapore, Saison International’s global operations span over Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil, with active investments in debt, equity, corporate venture capital, and technology.

Key Responsibilities

• Data & ML Engineering Leadership
• Build and scale ML engineering and data engineering functions.
• Establish MLOps frameworks for standardized, production-grade model development and monitoring.
• Ensure smooth model transition from data science experimentation to live deployment.
• Enterprise Decisioning PlatformDesign and operationalize a centralized decisioning platform that integrates low-code model development, AutoML, rule engines, and workflow automation.
• Enable DS & Risk teams to build, test, and deploy models with minimal engineering bottlenecks.Expand decisioning systems across functional pods—credit, pricing, collections, fraud, cross-sell, customer management—to drive consistent, explainable, and auditable decision-making.
• Ensure the platform is scalable, modular, and compliant with RBI regulations.Core Platform & Lifecycle Management
• 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.
• Operational Excellence
• Lead DataOps, L1/L2 support, and SRE teams to maintain >
  99.5% platform uptime.Implement automated testing, proactive monitoring, and self-healing systems.
• Optimize infra utilization and cloud cost efficiency.Business Delivery & Stakeholder EngagementAct as execution partner to the Head of Product & 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).

Required Skills & Qualifications

• 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 management—deployment, monitoring, retraining, and governance.
• Experience operationalizing 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 optimize infra utilization 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.

Required Skill Profession

Computer Occupations