Machine Learning Operations-Engineer II

GM Financial•Irving, TX

6h•Hybrid

About The Position

Design, build, and operate cloud-based MLOps capabilities supporting the full lifecycle of analytical and generative AI models. The role combines machine learning engineering, data engineering, and software engineering, with a focus on automation, scalability, governance, and production readiness. It uses technologies such as MLflow, Databricks, Azure Machine Learning, CI/CD pipelines, containerization, and event-driven architectures. The engineer partners with data science, IT, and business teams to deliver secure, compliant, and high-impact AI solutions. The position will be posted until filled.

Requirements

2–4 years of experience as a data scientist, machine learning engineer, or in a similar quantitative field.
High school diploma or equivalent.
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 such as RAG, prompt/version management, evaluation, safety, and monitoring.
Strong software and cloud engineering fundamentals, including CI/CD, Docker, and Kubernetes (AKS).
Experience with event-driven and streaming architectures and modern cloud-native design patterns.
Advanced skills in Python, SQL, and large-scale data platforms such as Spark, Delta, and lakehouse architectures.
Ability to clearly communicate technical trade-offs and connect AI delivery to business and financial outcomes.

Nice To Haves

Master’s degree in Computer Science, Engineering, Analytics, Mathematics, or a related discipline.
PhD.

Responsibilities

Build and maintain scalable, cloud-based MLOps platforms supporting analytical and GenAI models end to end.
Develop production-ready ML pipelines for training, deployment, monitoring, governance, and lifecycle automation.
Improve the speed, quality, and reliability of model development, experimentation, and operations.
Partner with model governance, security, and compliance teams to define and enforce MLOps standards.
Collaborate with data science, engineering, and business stakeholders to deliver solutions aligned with business needs.
Research, prototype, and evolve MLOps capabilities to drive innovation and measurable business value.