Senior Engineer, ML Ops

Position Profile
The Senior Engineer, ML Ops contributes to the development, automation, and deployment of Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) pipelines across both cloud and on-premises environments. This hands-on role focuses on building reproducible workflows, integrating monitoring tools, and enabling efficient model delivery. The position involves close collaboration with senior engineers and cross-functional teams, supporting AI systems deployed in commercial printing environments where resilience, real-time performance, and hardware-in-the-loop reliability are critical.
Job Duties and Responsibilities

• Deploy and maintain ML/DL/GenAI models using cloud and on-premises compute infrastructure.
• Assist in building and maintaining internal model-serving platforms for development teams.
• Implement containerized services using Docker and deploy to AWS Fargate and internal clusters.
• Write infrastructure-as-code using AWS CloudFormation and automate configuration for on-prem tools.
• Participate in unit and end-to-end testing of pipelines, services, and monitoring workflows.
• Support model monitoring and health tracking using AWS CloudWatch and internal observability tools.
• Document internal systems and operational processes to ensure maintainability and reproducibility.

Qualifications

• Bachelor's degree in a technical field with 1-5 years of experience, or a recent MS graduate.
• Strong Python programming skills, with working knowledge of pandas, SQL, and ML frameworks (e.g., scikit-learn).
• Exposure to CI/CD workflows and containerization practices.
• Familiarity with AWS services (e.g., Lambda, Glue, CloudWatch) and a willingness to learn on-prem integration.
• Interest or experience in maintaining AI systems used by developers or analysts.

Knowledge, Skills, and Abilities

• Solid understanding of model deployment fundamentals.
• Willingness to learn and work in hybrid infrastructure environments (cloud + on-prem).
• Foundational knowledge of model lifecycle management, container workflows, and observability practices.
• Strong communication and documentation skills, with a collaborative team mindset.

Working Conditions, Mental and Physical Demands

• Ability to manage job-related stressors and maintain performance under pressure.
• Work in environments adhering to regulatory standards or national guidelines (e.g., exposure to dust, fumes, or physical agents).
• Flexibility to travel as needed, work overtime, and adapt to new tools, systems, and assignments.
• Ability to handle multiple tasks concurrently and communicate clearly with peers, management, and customers.
• Maintain positive working relationships and demonstrate openness to diverse perspectives.
• Learn and apply new tools and equipment within a reasonable timeframe.
• Assume ownership of assignments and meet deadlines consistently.

The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, skills, efforts or working conditions associated with a job.