New

Advanced ML Ops Engineer

Relativity
United States, Illinois, Chicago
231 South LaSalle Street (Show on map)
Jan 09, 2026
Posting Type Hybrid Job Overview At Relativity, we build technology that helps people uncover the truth in complex data. Our software (SaaS) empowers legal professionals, governments, and organizations around the world to navigate high stakes matters with confidence, clarity, and integrity. By combining advanced AI, powerful analytics, and cloud-based technology, we help teams make sense of massive volumes of information and move critical work forward faster and more accurately. Every role at Relativity contributes to creating scalable, secure, and intelligent solutions with real-world impact-while fostering a culture where curiosity, collaboration, and inclusion thrive and where employees help shape the future of legal technology. The AI and Applied Sciences team at Relativity is dedicated to advancing intelligent systems that empower legal and compliance professionals. We focus on building scalable AI solutions that enhance data discovery, improve decision-making, and deliver measurable value to our customers. As an Advanced ML Ops Engineer, you will bridge the gap between core engineering and data-science teams, building and evolving the platforms, pipelines, and practices that turn research prototypes into reliable, scalable production solutions. You will own critical components of the ML lifecycle - from automated training pipelines to secure, monitored deployments - while collaborating closely with Senior and Lead engineers to drive continuous improvement across Relativity's AI platform. Job Description and Requirements Responsibilities Design & Build.Contribute to the design and implementation of ML/AI platforms, focusing on scalability, reliability, and security while standardizing GenAI workflows and model management processes. Cross-functional Collaboration.Partner with data scientists, product managers, security, and data-engineering teams to deliver high-impact ML solutions and ensure robust data protection and compliance. Automation & CI/CD.Implement and enhance CI/CD pipelines for models and data workflows using containerization, infrastructure-as-code, and orchestration tools such as Prefect or Airflow. Innovation & Prototyping.Prototype emerging ML Ops technologies to reduce costs, improve efficiency, and unlock new product capabilities; share findings and best practices across the AI organization. Operations & Monitoring.Deploy, monitor, and tune production models,establishinghealth, performance, and cost-optimization metrics. Technical Excellence & Mentorship.Participate in code and design reviews, contribute hands-on to projects, and mentor junior engineers while continuously developing your ownexpertise. Minimum Qualifications 3+ yearsof professionalsoftware-engineeringexperience, including1+yearin ML/AI orbigdataenvironments. ProficiencyinPython and C#, with production experience usingDocker. Practical experience deploying solutions onAWS, Azure,orGCPvia infrastructure-as-code tools (e.g., Terraform,Pulumi). Familiarity witha workfloworchestrationtool(Prefect, Airflow, etc.) and fundamentals ofKubernetes/Helm. Demonstrated ability to deploy,monitor, and troubleshoot ML models in production, collecting metrics for reliability and algorithmic health. Preferred Qualifications Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (Master's a plus). Experience with ML-lifecycle frameworks such asMLfloworKubeflow. Exposure tomodel-optimization techniques (quantization, pruning, compression) and/ordistributeddataframeworks (Spark, EMR, Kafka). Familiarity with deep-learning frameworks (TensorFlow,PyTorch) and secure, compliant data-processing environments. Relativity is committed to competitive, fair, and equitable compensation practices. This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives. The expected salary range for this role is between following values: $103,000 and $155,000 The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position. Suggested Skills: Engineering Principle, Hardware Integration, Innovation, Problem Solving, Process Improvements, Quality Assurance (QA), Research and Development, System Designs, Technical Documents, Troubleshooting