Lead Data Scientist / AI/ML Engineer (Tier 1 College)

Roles & Responsibilities

• Lead AI Product Pods across Credit Risk, Fraud, and Collections functions.
• Build and deploy production-scale Machine Learning systems for lending lifecycle decisioning.
• Own complete ML lifecycle including feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
• Design scalable distributed ML infrastructure, feature stores, model registries, and MLOps pipelines.
• Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, and recovery optimization.
• Drive model governance, monitoring, explainability, and compliance within BFSI regulatory standards.
• Collaborate with Product, Risk, Engineering, Data, and Business teams to deliver AI-driven business outcomes.
• Define AI platform architecture, operational excellence, SLAs, and incident management practices.
• Build, mentor, and scale high-performing AI Engineering and Data Science teams.

Ideal Candidate

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

Mandatory ( Education ) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

Pay: ₹408,717.29 - ₹7,000,000.00 per year

Application Question(s)

• Reason for change of last 2 organizations (Need detailed, not one word) ?
• Any offer in hand (If yes, What's Offered CTC & why still looking out) ?
• Reason to relocation to Pune (Need detail, not one word) ?
• Please share your 10th,12th and graduation percentages (%)
• What's your Date of Birth (DD/MM/YYYY) ?
• Did you complete B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) ?
• What is your notice period in days? (30)
• What is your current CTC in LPA?
• What is your expected CTC in LPA? (70)

Experience

• overall: 10 years (Required)

Work Location: In person