Position overview: 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.
- Technology stack: Cloud & AI Platforms (AWS): AWS SageMaker, EC2, EKS (Elastic Kubernetes Service), Lambda, S3, CloudWatch
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MLOps & Model Management: SageMaker Pipelines, MLflow, model registry and deployment frameworks
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Containerisation & Orchestration: Docker, Kubernetes (EKS)
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Monitoring & Observability: AWS CloudWatch, CloudTrail, Prometheus, Grafana, OpenTelemetry
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CI/CD & DevOps: AWS CodePipeline, CodeBuild, CodeDeploy, Jenkins, GitHub Actions
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Data & Integration: AWS Glue, Kinesis, EventBridge, REST APIs, SQL/NoSQL (RDS, DynamoDB)
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Security & Identity: IAM, AWS KMS, Secrets Manager, VPC security (subnets, NACLs, security groups)
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Resilience & Backup: AWS Backup, cross-region replication, DR strategies (multi-AZ / multi-region)
- Responsibilities: Provide L2/L3 production support for AI/ML models and data pipelines used in banking systems
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Monitor model performance, drift, data quality, and operational health of AI services
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Ensure stability and uptime of AI platforms supporting customer-facing and regulatory workloads
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Perform incident management, root cause analysis (RCA), and problem management in line with ITIL practices
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Collaborate with Data Science, Engineering, Risk, and Compliance teams
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Support secure deployment, release, and rollback of models in production
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Implement monitoring, alerting, and audit logging to meet regulatory and audit requirements
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Ensure adherence to data privacy, governance, and financial regulatory standards (e.g., GDPR, model risk frameworks)
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Support disaster recovery (DR) and business continuity (BCP) plans for AI workloads
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Identify opportunities for automation, operational efficiency, and cost optimization
- Requirements: Experience in production support / SRE / platform engineering, preferably in banking or financial services
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Strong understanding of AI/ML lifecycle and model operations (MLOps)
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Experience with cloud platforms (Azure preferred in banking), including secure workloads
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Proficiency in Python and scripting for debugging and automation
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Hands-on experience with Docker, Kubernetes, and microservices architectures
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Familiarity with MLOps tools (MLflow, Azure ML, SageMaker, etc.)
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Experience with monitoring & observability tools (CloudWatch, Splunk, Grafana, Prometheus)
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Knowledge of data pipelines, APIs, batch and real-time processing systems
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Experience with incident management tools (e.g., ServiceNow)
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Understanding of model risk management (MRM) and audit expectations
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Awareness of data governance, lineage, and controls
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Familiarity with security standards and identity access management (IAM)
- Nice to have: Exposure to AI governance frameworks and explainability tools
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Experience with fraud detection, credit risk, or financial analytics models
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Knowledge of secure DevOps (DevSecOps) practices
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Relevant certifications (AWS, MLOps)
