AI Production Support Engineer

About the Position

We are looking for an AI Production Support Engineer to support and operate AI/ML solutions within a regulated banking environment. The role focuses on ensuring high availability, resilience, compliance, and risk management of AI systems that support critical banking services.

Responsibilities

• Provide L2/L3 production support for AI/ML models and data pipelines used in banking systems
• Monitor model performance, drift, data quality, and operational health of AI services
• Ensure stability and uptime of AI platforms supporting customer-facing and regulatory workloads
• Perform incident management, root cause analysis (RCA), and problem management in line with ITIL practices
• Collaborate with Data Science, Engineering, Risk, and Compliance teams
• Support secure deployment, release, and rollback of models in production
• Implement monitoring, alerting, and audit logging to meet regulatory and audit requirements
• Ensure adherence to data privacy, governance, and financial regulatory standards (e.g., GDPR, model risk frameworks)
• Support disaster recovery (DR) and business continuity (BCP) plans for AI workloads
• Identify opportunities for automation, operational efficiency, and cost optimization

Requirements

• Experience in production support / SRE / platform engineering, preferably in banking or financial services
• Strong understanding of AI/ML lifecycle and model operations (MLOps)
• Experience with cloud platforms (Azure preferred in banking), including secure workloads
• Proficiency in Python and scripting for debugging and automation
• Hands-on experience with Docker, Kubernetes, and microservices architectures
• Familiarity with MLOps tools (MLflow, Azure ML, SageMaker, etc.)
• Experience with monitoring & observability tools (CloudWatch, Splunk, Grafana, Prometheus)
• Knowledge of data pipelines, APIs, batch and real-time processing systems
• Experience with incident management tools (e.g., ServiceNow)
• Understanding of model risk management (MRM) and audit expectations
• Awareness of data governance, lineage, and controls
• Familiarity with security standards and identity access management (IAM)

Nice to Have

• Exposure to AI governance frameworks and explainability tools
• Experience with fraud detection, credit risk, or financial analytics models
• Knowledge of secure DevOps (DevSecOps) practices
• Relevant certifications (AWS, MLOps)

Technologies

Cloud & AI Platforms (AWS): AWS SageMaker, EC2, EKS (Elastic Kubernetes Service), Lambda, S3, CloudWatch MLOps & Model Management: SageMaker Pipelines, MLflow, model registry and deployment frameworks Containerisation & Orchestration: Docker, Kubernetes (EKS) Monitoring & Observability: AWS CloudWatch, CloudTrail, Prometheus, Grafana, OpenTelemetry CI/CD & DevOps: AWS CodePipeline, CodeBuild, CodeDeploy, Jenkins, GitHub Actions Data & Integration: AWS Glue, Kinesis, EventBridge, REST APIs, SQL/NoSQL (RDS, DynamoDB) Security & Identity: IAM, AWS KMS, Secrets Manager, VPC security (subnets, NACLs, security groups) Resilience & Backup: AWS Backup, cross-region replication, DR strategies (multi-AZ / multi-region)