Job Summary:MLOps L2 Support Engineer to provide 24/7 production support for machine learning (ML) and data pipelines. The role requires on-call support, including weekends, to ensure high availability and reliability of ML workflows. The candidate will work with Dataiku, AWS, CI/CD pipelines, and containerized deployments to maintain and troubleshoot ML models in production.
Key Responsibilities:Incident Management & Support: - Provide L2 support for MLOps production environments, ensuring uptime and reliability.
- Troubleshoot ML pipelines, data processing jobs, and API issues.
- Monitor logs, alerts, and performance metrics using Dataiku, Prometheus, Grafana, or AWS tools such CloudWatch.
- Perform root cause analysis (RCA) and resolve incidents within SLAs.
- Escalate unresolved issues to L3 engineering teams when needed.
Dataiku Platform Management: - Manage Dataiku DSS workflows, troubleshoot job failures, and optimize performance.
- Monitor and support Dataiku plugins, APIs, and automation scenarios.
- Collaborate with Data Scientists and Data Engineers to debug ML model deployments.
- Perform version control and CI/CD integration for Dataiku projects.
Deployment & Automation: - Support CI/CD pipelines for ML model deployment (Bamboo, Bitbucket etc).
- Deploy ML models and data pipelines using Docker, Kubernetes, or Dataiku Flow.
- Automate monitoring and alerting for ML model drift, data quality, and performance.
Cloud & Infrastructure Support: - Monitor AWS-based ML workloads (SageMaker, Lambda, ECS, S3, RDS).
- Manage storage and compute resources for ML workflows.
- Support database connections, data ingestion, and ETL pipelines (SQL, Spark, Kafka).
Security & Compliance: - Ensure secure access control for ML models and data pipelines.
- Support audit, compliance, and governance for Dataiku and MLOps workflows.
- Respond to security incidents related to ML models and data access.
Required Skills & Experience: - Experience: 5+ years in MLOps, Data Engineering, or Production Support.
Dataiku DSS: Strong experience in Dataiku workflows, scenarios, plugins, and APIs.
Cloud Platforms: Hands-on experience with AWS ML services (SageMaker, Lambda, S3, RDS, ECS, IAM).
CI/CD & Automation: Familiarity with GitHub Actions, Jenkins, or Terraform.
Scripting & Debugging: Proficiency in Python, Bash, SQL for automation & debugging.
Monitoring & Logging: Experience with Prometheus, Grafana, CloudWatch, or ELK Stack. - Incident Response: Ability to handle on-call support, weekend shifts, and SLA-based issue resolution.
Preferred Qualifications: - Containerization: Experience with Docker, Kubernetes, or OpenShift.
ML Model Deployment: Familiarity with TensorFlow Serving, MLflow, or Dataiku Model API. - Data Engineering: Experience with Spark, Databricks, Kafka, or Snowflake.
- ITIL/DevOps Certifications: ITIL Foundation, AWS ML certifications; Dataiku certification
Work Schedule & On-Call Requirements: - Rotational on-call support (including weekends and nights).
- Shift-based monitoring for ML workflows and Dataiku jobs.
- Flexible work schedule to handle production incidents and critical ML model failures.