Design, build, and operate cloud-based MLOps capabilities that support the full lifecycle of analytical and generative AI models
Blend machine learning engineering, data engineering, and software engineering with a focus on automation, scalability, governance, and production readiness
Work with technologies such as MLflow, Databricks, Azure Machine Learning, CI/CD pipelines, containerization, and event-driven architectures
Partner closely with data science, IT, and business teams to deliver secure, compliant, and high-impact AI solutions

2-4 years as Data Scientist or machine learning engineer or similar quantitative field required
High School Diploma or equivalent required
Master’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline preferred, PhD preferred
Proven hands-on experience across the full ML/MLOps lifecycle, including MLflow and platforms such as Databricks, Azure ML, or SageMaker
Experience operationalizing GenAI solutions, including LLM patterns (e.g., RAG), prompt/version management, evaluation, safety, and monitoring
Strong software and cloud engineering fundamentals, including CI/CD, containerization (Docker), and Kubernetes (AKS)
Experience with event-driven and streaming architectures and modern cloud-native design patterns
Advanced skills with Python, SQL, and large-scale data platforms (e.g., Spark, Delta, lakehouse architectures)
Ability to clearly communicate technical trade-offs and connect AI delivery to business and financial outcomes.

Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.

Machine Learning Operations Engineer II

Key skills