AI Production Support Engineer
- Bengaluru
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Position overview
Technology stack
Cloud and AI Platforms: Amazon SageMaker, Amazon EC2, Amazon EKS, AWS Lambda, Amazon S3, Amazon CloudWatch
MLOps and Model Management: SageMaker Pipelines, MLflow, model registries, and deployment frameworks
Containerization and Orchestration: Docker, Kubernetes, Amazon EKS
Monitoring and Observability: Amazon CloudWatch, AWS CloudTrail, Prometheus, Grafana, OpenTelemetry
CI/CD and DevOps: AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy, Jenkins, GitHub Actions
Data and Integration: AWS Glue, Amazon Kinesis, Amazon EventBridge, REST APIs, SQL, NoSQL, Amazon RDS, Amazon DynamoDB
Security and Identity: IAM, AWS Key Management Service (KMS), AWS Secrets Manager, VPC security, subnets, network ACLs, and security groups
Resilience and Backup: AWS Backup, cross region replication, disaster recovery strategies, multi Availability Zone and multi region architectures
Responsibilities
- Provide L2 and L3 production support for AI and ML models and data pipelines used in banking systems
- Monitor model performance, model drift, data quality, and the operational health of AI services
- Ensure the stability and availability 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 the 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, including GDPR and model risk management frameworks
- Support disaster recovery (DR) and business continuity planning (BCP) for AI workloads
- Identify opportunities for automation, operational efficiency, and cost optimization
Requirements
- Experience in production support, Site Reliability Engineering (SRE), or platform engineering, preferably within banking or financial services
- Strong understanding of the AI and ML lifecycle and MLOps practices
- Experience with AWS cloud services and secure cloud workloads
- Proficiency in Python and scripting for troubleshooting, debugging, and automation
- Hands on experience with Docker, Kubernetes, and microservices architectures
- Familiarity with MLOps platforms and tools such as MLflow and Amazon SageMaker
- Experience with monitoring and observability tools including Amazon CloudWatch, Splunk, Grafana, and Prometheus
- Knowledge of data pipelines, APIs, and both batch and real time processing systems
- Experience with incident management platforms such as ServiceNow
- Understanding of Model Risk Management (MRM) and audit requirements
- Knowledge of data governance, data lineage, and control frameworks
Nice to have
- Exposure to AI governance frameworks and model explainability tools
- Experience supporting fraud detection, credit risk, or financial analytics models
- Knowledge of DevSecOps practices and secure software delivery
- Relevant AWS, MLOps, or cloud related certifications
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Vacation
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Health insurance
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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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