AWS Mlops Solution Architect

AI/ML Subject Matter Expert

Act as the AI/ML subject matter expert across teams, providing guidance, best practices, and hands-on support throughout the ML lifecycle. Collaborate with data science and engineering teams to develop, deploy, and maintain ML models using AWS services such as SageMaker, Lambda, EKS, and Step Functions. Architect and implement scalable, secure, and maintainable ML solutions in the AWS cloud environment using Infrastructure as Code (Terraform) and automation tools. Support experimentation, tracking, and reproducibility using tools like MLflow. Serve as a liaison between technical and non-technical teams, effectively communicating AI/ML strategies, timelines, and progress. Contribute to CI/CD pipelines and ML operations using GitHub Actions and Python. Apply Agile best practices in task management, sprint planning, and cross-functional collaboration. (Bonus) Provide solution architecture support and input during project planning and review.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

Skills and Requirements

• 5+ years of experience in cloud engineering or DevOps with deep knowledge of AWS infrastructure and services.
• Experience designing and implementing robust, scalable, and automated MLOps solutions within the AWS cloud environment.
• Strong hands-on experience with machine learning operations (MLOps) in cloud environments.
• Experience with AWS ML services like SageMaker, Step Functions, EKS, and Lambda.
• Familiarity with the end-to-end ML lifecycle: data ingestion, model training, evaluation, deployment, and monitoring.
• Hands-on experience with MLflow or similar model tracking platforms.
• Proficient with GitHub, GitHub Actions, and Agile tools like Jira.
• Excellent communication and stakeholder management skills.
• AWS certifications, especially in Machine Learning or Solutions Architecture.
• Experience in designing end-to-end cloud-native ML solutions.
• Prior experience as a Solution Architect or working in an architecture-focused role.