Location:

Location: India/Dubai

Reports To:

Chief Data Officer / Head of Data & AI

Role Overview

We are looking for an experienced AI Ops Lead to drive the operationalization of Artificial Intelligence and Generative AI solutions across our banking ecosystem. This role will ensure AI models that support credit decisioning, fraud detection, financial crime compliance, KYC, customer engagement, and back-office automation are reliable, explainable, secure, and regulatory compliant.

The AI Ops Lead will build and run the operational backbone for AI adoption — establishing frameworks for deployment, monitoring, retraining, bias/fairness governance, and auditability — while ensuring seamless integration of AI into critical banking processes.

Key Responsibilities

1. AI Operations & Lifecycle Management

- Lead the end-to-end AI/ML lifecycle: model deployment, monitoring, drift detection, retraining, and retirement.
- Implement observability practices for credit scoring models, fraud detection engines, and GenAI copilots (e.g., for KYC or customer service).
- Define SLAs and ensure high availability of AI systems used in critical decision-making.

2. Platform & Infrastructure

- Design and manage scalable AI Ops pipelines across cloud, hybrid, and on-prem banking environments.
- Oversee CI/CD/CT pipelines for AI models, ensuring smooth rollouts of updates for compliance and risk engines.
- Integrate AI-specific tooling: vector databases for knowledge retrieval, LLMOps frameworks for prompt orchestration, and observability dashboards.

3. Governance, Risk & Compliance

- Establish operational controls for regulatory compliance (e.g., Basel III, GDPR, local central bank guidelines).
- Implement AI model auditability and explainability frameworks to meet internal risk and regulator demands.
- Collaborate with Compliance, Risk, and Internal Audit teams to ensure AI solutions meet ethical and legal standards.

4. Leadership & Collaboration

- Lead and mentor an AI Ops squad (engineers/analysts) responsible for production operations.
- Work closely with Data Scientists, Credit Risk, Compliance, and IT to bring prototypes to production at scale.
- Act as a trusted advisor on AI Ops best practices within the bank and across industry forums.

Qualifications & Skills

Education & Experience

- Bachelor’s/Master’s in Computer Science, Data Science, or Engineering.
- 8–12 years of experience in Data/AI/ML Ops, with at least 3–5 years in financial services or other regulated industries.
- Proven track record of operationalizing AI in banking use cases (credit risk, fraud, AML, collections, compliance).

Technical Skills

- Expertise in MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, Databricks ML).
- Cloud experience across AWS, GCP, Azure with container orchestration (Kubernetes, Docker).
- Familiarity with GenAI/LLMOps stacks (LangChain, LangGraph, CrewAI, PromptLayer, Weights & Biases).
- Strong programming (Python) and ML framework knowledge (TensorFlow, PyTorch, Scikit-learn).
- Knowledge of banking regulatory frameworks (model risk management, explainability, bias/fairness).

Soft Skills

- Excellent leadership, stakeholder engagement, and risk communication skills.
- Ability to balance business-critical reliability with innovation enablement.
- Strong documentation and reporting mindset for audit/regulatory reviews.

KPIs & Success Measures

- % of AI models in production with automated monitoring for drift, bias, and compliance.
- Reduction in AI-related incidents/downtime impacting credit, KYC, or compliance operations.
- Audit readiness and regulator satisfaction scores.
- Faster time-to-production for AI models while maintaining risk and compliance standards.
- Optimized cost and efficiency of AI infrastructure.