ML Ops Support Engineer

Responsibilities

• 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.
• 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.
• 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.
• Monitor and support 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).
• Ensure secure access control for ML models and data pipelines.
• Respond to security incidents related to ML models and data access.

Requirements

• 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