Position Summary

We are seeking an experienced and highly motivated DevOps / MLOps Engineer Lead to join our dynamic Data Science and AI team.  In this role, you will be pivotal in creating and maintaining robust, scalable, and efficient CI/CD pipelines for our machine learning models and data processing workflows.  You will collaborate with cross-functional teams to streamline and automate the end-to-end deployment processes, ensuring our AI/ML initiatives are delivered with high quality and speed.

Key Responsibilities

• Develop and Implement CI/CD Pipelines: Design, build, and maintain continuous integration and deployment pipelines for machine learning models and data processing workflows.
• Automation and Orchestration: Develop and continuously improve automation solutions to enable teams to build and deploy code efficiently and consistently.
• Promote DevSecOps Principles: Foster a DevSecOps culture across the Analytics & Innovation organization, ensuring security is integrated into the development process.
• Lifecycle Streamlining: Streamline the data science and development lifecycles by identifying and resolving pain points and productivity barriers.
• Collaboration: Work closely with data scientists, data engineers, and software developers to integrate and deploy machine learning models into production.
• Monitoring and Troubleshooting: Implement monitoring and logging solutions to ensure the health and performance of deployed models and systems, and troubleshoot issues as they arise.
• Security and Compliance: Ensure the security and compliance of data and infrastructure, adhering to industry best practices and regulatory requirements.
• Documentation: Maintain comprehensive documentation of systems, processes, and workflows to facilitate knowledge sharing and collaboration.

Requirements:

• Education: Bachelor’s Degree in Computer Science, Engineering, or a related field.
• Experience: 5+ years of experience in DevOps, MLOps, or a related field.
• Azure DevOps and AzureML experience.

Technical Expertise:

• Proficiency in cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Strong programming skills in Python, Bash, PowerShell or other scripting languages.
• Experience with infrastructure as code (Terraform, ARM).

Tool Proficiency:

• Familiarity with CI/CD tools (Jenkins, GitHub Actions, ADO Pipelines).
• Knowledge of machine learning frameworks (TensorFlow, PyTorch) and data processing tools (Apache Spark, Airflow).
• Problem-Solving: Excellent problem-solving and analytical skills, with a focus on delivering practical and efficient solutions.

Preferred Experiences:

• Advanced Analytics Tools: Experience with advanced analytics tools and methodologies, including monitoring and logging solutions (Azure Monitor, Prometheus, Grafana).
• Agile Methodologies: Experience working in Agile development environments.
• Communication: Strong verbal and written communication skills, capable of articulating complex technical concepts to both technical and non-technical stakeholders.
• Team Collaboration: A collaborative mindset with a track record of working effectively within diverse teams.

Other Qualifications:

• AZ-400 DevOps Engineer Certification is desired.
• Experience with Data Science and Machine Learning teams is desired.