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.

Key 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 optimisation

Required Skills & Experience :

- 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)

Essential Knowledge (Including Tooling) :

- 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)

- 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)