AI Production Support Engineer

Position overviewWe 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, Cloud
• Watch
• MLOps & Model Management: SageMaker Pipelines, MLflow, model registry and deployment frameworks
• Containerisation & Orchestration: Docker, Kubernetes (EKS)

Monitoring & Observability: AWS CloudWatch, CloudTrail, Prometheus, Grafana, Open

• TelemetryCI/CD & DevOps: AWS Code
• Pipeline, CodeBuild, CodeDeploy, Jenkins, GitHub Actions
• Data & Integration: AWS Glue, Kinesis, Event
• Bridge, 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)

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)

What We Offer:

Vacation days: Up to 26 business days per year.10 illness/special days off per year (fully paid, no medical papers needed) for all contract types

Health and life insurance (Luxmed)

MyBenefit platform with Multisport option

• Internal psychological support service
• English language classes from the first working day
• Access to external learning platforms: O’

Reilly, LinkedIn Learning, Udemy, and a wide catalog of diverse internal training

• Flexible workplace: work from the office, from home, or choose a hybrid optionTech Skills Mentoring Program
• Opportunities to develop as a public speaker, mentor, or technical interviewer
• Fully paid idle (bench) when not involved in a project
• Certification reimbursement (AWS, GCP, Microsoft, etc.)