AI Production Support Engineer
- Belgrade
- Bengaluru
- Cluj-Napoca
- Krakow
- Larnaca
- Lodz
- Lublin
- Remote.Bulgaria
- Remote.Georgia
- Remote.Poland
- Riga
- Sofia
- Tbilisi
- Varna
- Warsaw
- Wroclaw
- Yerevan
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Position overview
Technology stack
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)
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)
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We offer
Vacation
As per the laws of your country. We do ask you to take a proper rest
Health insurance
We help you to take out an insurance policy for you and your loved ones
Sick pay
10 days without a doctor's note, afterwards - as per the laws of your country
Time off for state holidays
According to the official calendar, regardless of the client’s schedule
Pleasant environment
Two large corporate parties and many small get-togethers for colleagues
Comfort service
Solving technical and everyday problems at work
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