Software Engineer 2 - AI DevOps Engineer

Job Description A client of Insight Global's is looking to hire a DevOps AI Engineer to join their team. This person will be responsible for the design, development, and deployment of AI agents and ML systems. The agents and systems will be used to automate tasks within this organization and cross other internal teams. This organization is in initial stages of implementing AI into its devops environment. Additionally, you'll be responsible for managing IaC, their hybrid cloud environment, and partner with security teams to ensure compliance. This role is onsite 4x a week in Denver. This is a direct hire opportunity with a salary range of $112K to $135k. 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 learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements - 3 Years of experience in devops, fullstack engineering, or backend engineering doing programming in python, java, or scala. - 2 years of experience building AI/ML systems or agents using ML frameworks such as TensorFlow, scikitlearn, pytorch, or similar. - Experience doing production deployments in AWS, containerization in Docker, and orchestration in Kubernetes. - Understanding of data engineering practices like data modeling and warehousing using data bricks, spark, Kafka, and SQL. Experience in media, streaming, adtech, or martech industries.

Created: 2026-05-02