Advanced ML Ops Engineer

Relativity

2d • Hybrid

About The Position

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.

Requirements

• 3+ years of professional software-engineering experience, including 1+ year in ML/AI or big data environments.
• Proficiency in Python and C#, with production experience using Docker.
• Practical experience deploying solutions on AWS, Azure, or GCP via infrastructure-as-code tools (e.g., Terraform, Pulumi).
• Familiarity with a workflow orchestration tool (Prefect, Airflow, etc.) and fundamentals of Kubernetes/Helm.
• Demonstrated ability to deploy, monitor, and troubleshoot ML models in production, collecting metrics for reliability and algorithmic health.

Nice To Haves

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field (Master’s a plus).
• Experience with ML-lifecycle frameworks such as MLflow or Kubeflow.
• Exposure to model-optimization techniques (quantization, pruning, compression) and/or distributed data frameworks (Spark, EMR, Kafka).
• Familiarity with deep-learning frameworks (TensorFlow, PyTorch) and secure, compliant data-processing environments.

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, establishing health, 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 own expertise.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees