Artificial Intelligence Manager

Primary Purpose

Owns the strategy, architecture, and delivery of end-to-end AI/ML solutions; ensures compliance with banking regulations and internal policies; mentors juniors; partners with business owners to realize financial impact.

Key Responsibilities

Strategy & Prioritization

• Participate in build and maintain AI roadmaps aligned to business OKRs and regulatory requirements.
• Prioritize high-ROI use cases (risk, fraud, AML, CX, revenue uplift, cost reduction).

Solution Architecture

• Define target data and MLOps architecture (data lake/warehouse, feature store, model registry, CI/CD).
• Select tooling stack (e.g., Python, SQL, Spark, MLflow, feature store, Docker/Kubernetes, Airflow/Argo, API gateways, math, LLM tools, Gen AI tools, Agentic AI tools , … ).

Data & Feature Governance

• Establish data quality SLAs, lineage, cataloging, PII handling, and approval workflows.
• Define and curate reusable feature store assets with business definitions.

Model Development & Review

• Lead modeling approach selection; design experiments and validation plans.
• Enforce controls: bias testing, stability tests, challenger models, reject inference (where appropriate), and backtesting.
• Ensure explainability (SHAP/feature attributions), documentation, and approval packs for Model Risk Management.

Deployment & MLOps

• Standardize CI/CD for models, automated tests, reproducibility, canary/blue-green releases, and rollbacks.
• Define monitoring KPIs: performance, calibration, drift, data quality, and fairness; design alerting and retraining triggers.

Risk, Compliance, and Security

• Align with regulatory expectations (e.g., model governance, fair lending principles, AML/CTF).
• Ensure secure data handling: encryption, masking, segregation of duties, access reviews.

Business Integration & Value Realization

• Translate model outputs into decisions, thresholds, and policies; integrate with decision engines and channels.
• Track realized impact with finance (uplift, loss reduction, approval rates, NPLs, fraud savings, CX metrics).

Leadership & Mentorship

• Coach juniors via code reviews, pair programming, and learning plans.
• Build documentation, templates, and runbooks.

Stakeholder Management

• Communicate risks, timelines, and outcomes to executives; coordinate with IT, InfoSec, Audit and AI committee.

Required Skills & Experience

• 7+ years in banking AI/ML or fintech; successful production deployments in credit risk/fraud/AML/marketing analytics is preferred.
• Strong in Python/SQL; experience with Spark; containerization (Docker), CI/CD; orchestration (Airflow/Argo); APIs (REST/GraphQL), LLM / Gen AI tools.
• Solid grasp of feature engineering for financial data, time-series methods, imbalanced classification, uplift modelling, and behaviour analysis.
• Cop with all AI new technologies, concepts with converting latest technologies to real life applications.
• MRM/Responsible AI: bias testing, interpretability, stability, documentation, and audit readiness.
• On-prem patterns; security and privacy best practices.
• Communication and leadership across technical and non-technical stakeholders.