ML Ops Support Engineer

• Mandatory Skills:

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.

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.