Lead Machine Learning Engineer - MLOps

We are seeing a highly skilled Machine Learning Engineer / MLOps Engineer to help scale and operationalize our cloud-based ML platform, with an immediate focus on managing and optimizing our AWS Sage Maker environment. This role is ideal for someone with a strong foundation in machine learning who is passionate about building production-ready systems, designing robust pipelines, deploying infrastructure as a code, and enabling seamless model integration through APOs and data workflows.

You will partner closely with data science teams to bridge the gap between experimentation and production, leveraging AWS services, serverless architectures, and modern CI/CD practices to delivery scalable, reliable, and efficient ML solutions.

• Manage and optimize the AWS Sage Maker platform used by data science teams for model training and experimentation
• Design, build, and maintain ML pipelines for training, testing, and deployment of models
• Develop and maintain infrastructure as code using Terraform
• Build and support CI/CD pipelines (ideally using Git Hub Actions) for ML and application deployment
• Create and maintain APIs and services to enable interaction with ML models
• Design and implement data processing pipelines to support model workflows
• Operate and support production-grade cloud infrastructure
  , ensuring reliability, scalability, and performance
• Collaborate closely with data scientists to bridge the gap between model development and production deployment

• Experience with Amazon Sage Maker for model training and deployment
• Proficiency with Terraform for infrastructure provisioning
• Experience managing production systems/infrastructure
• Experience building CI/CD pipelines (preferably with Git Hub Actions)
• Strong understanding of serverless architectures
  , including:
  • API Gateway
  • AWS Lambda
  • SQS
  • DynamoDB
• Programming skills in Python or similar languages commonly used in ML workflows
• Background in Machine Learning, Data Science, or GenAI (practical exposure preferred over theoretical)
• Experience operationalizing ML/GenAI models in production environments
• Experience building scalable backend systems in AWS
• Familiarity with event-driven and distributed system design

The salary range for this position takes into consideration a variety of factors, including but not limited to skill sets, level of experience, applicable office location, training, licensure and certifications, and other business and organizational needs. The new hire salary range displays the minimum and maximum salary targets for this position across all US locations, and the range has not been adjusted for any specific state differentials.

It is not typical for a candidate to be hired at or near the top of the range for their role, and compensation decisions are dependent on the unique facts and circumstances regarding each candidate. A reasonable estimate of the current salary range for this position is $73,008 to $170,640. Please note that the salary range posted reflects the base salary only and does not include benefits or any potential variable compensation programs.

Information regarding the benefits available for this position are in our benefits overview.

Disclaimer: The above statements are not intended to be a complete statement of job content, rather to act as a guide to the essential functions performed by the employee assigned to this classification. Management retains the discretion to add or change the duties of the position at any time.

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employees to perform their expected job duties is absolutely not tolerated. Disability Accommodations:
Perficient is committed to providing a barrier-free employment process with reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or accommodation due to a disability, please contact us.