National Lead - AI Unit

Job Purpose


We are looking for a leader who can build and run AI product Pods for Credit Risk, Fraud Risk Management, and collection & recovery. This role will power next-generation decisioning across lending lifecycle- understanding lifecycle- underwriting, portfolio monitoring, fraud controls, early warning, allocation strategy optimization, and recovery uplift ?? by delivering production-grade ML systems with measurable impact. This is highly cross-functional role requiring deep technical leadership, strong execution discipline, and hands-on experience operating large scale distributed machine learning frameworks. (Training + fine-tuning + serving) under BFSI governance, security, and model risk constraints.

Duties and Responsibilities


Own the AI Pod operating model across Credit Risk, Fraud/FRM, and collections/Recovery: outcomes,

roadmap, delivery cadence, and cross-team dependencies.


Lead end-to-end model lifecycle: problem framing, feature strategy, training, evaluation, development,

monitoring, and continuous improvement with clear scorecards per use-case.


Build large-scale ML systems: distributed training pipelines, feature stores, model registry, CI/CD for ML, and

scalable batch + near-real-time scoring services.


Deliver Credit & Risk models: application/behavior risk models, limit assignment, early warning signals,

portfolio monitoring, and policy optimization.


Deliver Frau & FRM systems: fraud propensity/risk scoring, anomaly detection, identity/device/channel

signals using Graph Machine Learning.


Deliver collection & recovery optimization: roll-rate/cure/flow models, contactability, propensity-to-pay and

recovery forecasting.



Define operating models: SLIs/SLOs, incident response, and stakeholder cadence.


Hire, develop, and scale the team: drive standards for quality, safety, and reliability.

Required Qualifications and Experience


Basic Qualifications:

Bachelor??s/Master??s in CS/Math/Engineering (PhD preferred in Large scale Machine learning systems)

10+ years experience in Data Science /Applied ML/ ML Engineering with proven leadership delivering

production ?? grade ML system at scale.

Demonstrated success shipping models with measurable business impact in credit risk, fraud/FRM, and /or

collection & recovery.







Required Skills & Competencies Core (must-have) Large-scale model training & Fine-tuning: experience with distributed training, efficient fine -tuning patterns, model versioning, reproducibility, and cost/performance trade-offs. ML evaluation rigor: calibration, stability/drift, bias/fairness checks, leakage prevention, robust back-testing, and champion-challenger frameworks. Production mindset: ability to translate business objectives into ML systems with strong monitoring, alerting, and operational playbooks. Engineering & Tooling Strong coding ability in Python (and Java/Scala as needed); ability to prototype rapidly and productize. Distributed systems knowledge: scaling, caching, sharding, HA, performance tuning for both training and serving. Experience with common stacks: feature stores, model registry, experiment tracking, vector/graph where relevant, stream/batch processing Kubernetes, CI/CD. Observability: logging/metrics/tracing, incident management, SLO-driven operations. Experience with data and compute stacks: Spark, Kafka/streaming, Lakehouse/warehouse, APIs/microservices. Governance, Security, and Compliance Designing for BFSI constraints: PII handling, policy enforcement, auditability, access controls. Risk-aware engineering mindset: safe tool execution, approval workflows, and secure-by-design, approval workflows, and secure by design patterns. Leadership Behaviors High ownership, structured thinking, and ability to drive clarity in ambiguous environments. Strong program management