ML Ops Support Engineer

Role Title: ML Ops Support Engineer

Location: Reading, PA-Onsite

Mandatory Skills:

ML Ops 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 ML Ops 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 (Sage Maker, 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 ML Ops workflows.

• Respond to security incidents related to ML models and data access.

Required Skills & Experience:

✅ Experience: 5 years in ML Ops, 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 (Sage Maker, 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, ML flow, 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.