AI Engineer

Summary

End-To-End Ai And Ml Solution Ownership

• Lead development of predictive and anomaly detection models for risk decisioning across retail lending and cards.
• Own the full ML lifecycle: EDA → feature engineering → model training → evaluation → calibration → deployment → monitoring → retraining.
• Implement champion–challenger frameworks and controlled rollout strategies to continuously improve model performance.
• Deliver actionable outputs (risk scores, key drivers, reason codes, prioritized worklists) for business and governance stakeholders.

Data Engineering & Feature Pipelines

• Design and build scalable, reliable data pipelines and reusable feature stores.
• Ensure strict time alignment and leakage prevention in feature engineering.
• Implement robust data quality frameworks (validation, reconciliation, completeness checks, exception handling).
• Collaborate with reporting and data teams while independently developing engineering components when required.

Production Deployment & Mlops

• Establish and manage MLOps practices: CI/CD, automated testing, model registry, and reproducible pipelines.
• Monitor model performance, drift (data/concept), and calibration stability; define retraining triggers.
• Maintain operational readiness through runbooks, alerting, and incident response support.

Explainability, Controls & Model Governance

• Implement explainable AI (e.g., SHAP) and translate outputs into audit ready reason codes.
• Produce complete model governance documentation (validation reports, testing evidence, monitoring dashboards).
• Ensure compliance with Model Risk Management (MRM) standards including:
• Human in the loop controls
• Full decision traceability
• Data security (encryption, RBAC, least privilege access)
• Partner with Risk and Compliance to strengthen governance frameworks.

Enterprise Integration

• Integrate ML services into enterprise systems via APIs and batch pipelines.
• Design solutions with strong auditability, logging, and data lineage.
• Collaborate with IT and Security teams to ensure compliant deployments, preferably on OCI.

Experience

• 7–10 years of experience, with 5+ years in ML engineering/data engineering within financial services.
• Proven track record of deploying multiple ML models into production environments.

Data Engineering

• Strong SQL and data modeling expertise.
• Experience building scalable feature pipelines with data quality, reconciliation, and lineage controls.

Machine Learning

• Deep expertise in feature engineering, time-based validation, and leakage prevention.
• Strong experience with:
• Supervised learning & anomaly detection
• Model evaluation (AUC, KS, precision/recall, lift, stability metrics)
• Probability calibration and challenger modeling
• Familiarity with synthetic data generation (with governance controls).

Explainability & Responsible Ai

• Hands-on experience with SHAP and model interpretability techniques.
• Experience implementing drift monitoring, bias detection, and validation standards.

Mlops & Production Engineering

• Experience deploying models via APIs/batch pipelines with secure configurations.
• Strong understanding of CI/CD for ML, versioning, rollback, and reproducibility.
• Experience with cloud platforms (OCI preferred).