What are the responsibilities and job description for the ML Ops Engineer/Cloud Engineer/DevOps Engineer position at VDart, Inc.?
Job Details
Job Title: ML Ops Support Engineer
Location: Reading, PA Onsite
Duration: 8 months
Job Description:
- ML Ops Support Engineer
- Dataiku
- CI/CD & Automation
- AWS ML services
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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
