Artificial Intelligence Manager

Primary PurposeOwns 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 ResponsibilitiesStrategy & PrioritizationParticipate 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 ArchitectureDefine 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 GovernanceEstablish data quality SLAs, lineage, cataloging, PII handling, and approval workflows.Define and curate reusable feature store assets with business definitions.Model Development & ReviewLead 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 & MLOpsStandardize 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 SecurityAlign 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 RealizationTranslate 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 & MentorshipCoach juniors via code reviews, pair programming, and learning plans.Build documentation, templates, and runbooks.Stakeholder ManagementCommunicate risks, timelines, and outcomes to executives; coordinate with IT, InfoSec, Audit and AI committee.Required Skills & Experience7+ 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.