Position overview: We are looking for an AI Production Support Engineer to support and operate AI/ML solutions within a regulated banking setting. 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
- 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)

📌 AI Production Support Engineer (India)
🏢 DataArt
📍 India