Mid-Level AI/ML Ops Engineer

Our client is a global performance marketing organization operating at the intersection of brand marketing, technology, and analytics, helping businesses design and manage data-driven marketing and brand strategies. They are hiring for a Mid-Level AI/ML Ops Engineer to build and maintain the infrastructure, deployment pipelines, monitoring, and cloud systems that gets AI and machine learning models into production reliably, securely, and at scale. This is a hands-on opportunity to own the operational backbone of a growing AI practice, working side by side with AI Engineers and the AI Tech Lead rather than setting architecture in isolation. You will take deployment and infrastructure requirements and turn them into dependable, monitored production systems, all while deepening your expertise across MLOps, cloud AI platforms, and LLM deployment. It is a great fit for someone who is detail oriented and reliability focused, stays calm and methodical when production issues come up, and wants room to grow into a stronger voice on the operational side of AI/ML. Required Skills & Experience Bachelor’s degree in Computer Science, Engineering, or a related field, or comparable experience 3-5 years of experience in DevOps, MLOps, data engineering, or a related infrastructure/operations role Hands-on experience deploying machine learning models into production environments Working knowledge of Databricks and cloud AI platforms (AWS preferred, including familiarity with services like Bedrock) Experience with containerization and orchestration tools (Docker, Kubernetes or equivalent) Proficiency in Python and familiarity with CI/CD tooling Experience with monitoring and observability tooling for production systems Solid understanding of data analytics fundamentals Desired Skills & Experience Familiarity with LLM deployment considerations (latency, cost, versioning) Experience with SQL What You Will Be Doing Tech Breakdown Databricks and AWS cloud AI platforms, including Bedrock Docker and Kubernetes (or equivalent orchestration) Python and CI/CD tooling Monitoring and observability platforms Daily Responsibilities Build and maintain CI/CD pipelines for deploying AI/ML models into production Implement monitoring and observability to catch performance degradation, drift, and failures early Manage cloud infrastructure supporting AI workloads, including containerized services and cloud AI platforms Collaborate with AI Engineers and the AI Tech Lead to translate requirements into deployment plans Troubleshoot production issues and support model versioning, reproducibility, and rollback processes Monitor and optimize the cost and resource efficiency of AI/ML workloads, flagging operational risks before they become incidents The Offer Bonus eligible You Will Receive The Following Benefits Medical, Dental, and Vision Insurance Vacation Time Stock Options Applicants must be currently authorized to work in the US on a full-time basis now and in the future. Posted By: McIver Harris