Advanced AI Engineer

Relativity•Illinois, IL

4d•$103,000 - $155,000•Hybrid

About The Position

As an Advanced AI Engineer, you will bridge the gap between core engineering and data science teams, building and evolving the platforms, pipelines, and practices that transform research prototypes into reliable, scalable production solutions. You will own critical components of the machine learning lifecycle, including automated training pipelines, secure deployments, monitoring, and operational excellence. Working closely with Senior and Lead Engineers, you will help drive continuous improvement across Relativity’s AI platform while contributing to technical strategy, implementation, and innovation.

Requirements

3+ years of professional software engineering experience, including at least 1 year working in ML/AI or big data environments.
Proficiency in Python, Java, or C#.
Production experience using Docker.
Experience deploying cloud-based solutions on AWS, Azure, or GCP.
Experience using infrastructure-as-code tools such as Terraform or Pulumi.
Familiarity with workflow orchestration platforms such as Prefect, Airflow, or similar technologies.
Understanding of Kubernetes and Helm fundamentals.
Experience deploying, monitoring, and troubleshooting machine learning models in production environments.
Ability to collect and analyze metrics related to model reliability and algorithm health.
Strong collaboration and communication skills with cross-functional stakeholders.

Nice To Haves

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

Responsibilities

Contribute to the design and implementation of ML/AI platforms with a focus on scalability, reliability, security, and standardized GenAI workflows.
Partner with data scientists, product managers, security teams, and data engineers to deliver high-impact machine learning solutions.
Implement and improve CI/CD pipelines for machine learning models and data workflows using containerization, infrastructure-as-code, and orchestration technologies.
Build and enhance automated model training, deployment, and lifecycle management processes.
Prototype and evaluate emerging MLOps technologies to improve efficiency, optimize costs, and enable new product capabilities.
Deploy, monitor, tune, and troubleshoot production machine learning models.
Establish and track health, performance, reliability, and cost optimization metrics for AI systems.
Participate in code reviews and design reviews while contributing directly to implementation efforts.
Mentor junior engineers and share best practices across the engineering and AI organizations.
Continuously learn and apply new technologies, tools, and techniques to improve the AI platform.