AI Engineer

Summary

The AI Engineer will design, build, and deploy production grade AI/ML solutions for Finance House Group. This is a hands-on technical leadership role within a lean, agile team, owning the full lifecycle of AI solutions from problem definition and data pipelines to model deployment, monitoring, and lifecycle management. The role requires strong alignment with regulatory expectations, ensuring governance, explainability, auditability, and security across all implementations.

Key Responsibilities

End-to-End AI/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.

Qualifications

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).