Senior Machine Learning Operations Engineer

Job-:
Technology

Location:

Irving, TX, United States Workplace:
Hybrid

Schedule:

Full time Posted:
Why GMF Technology?

Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

This position will be posted until filled. About the Role Apply AI/ML Engineering, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain ML and LLM Operations capabilities.  This position requires an understanding of the Data Science model development and Gen AI/LLM driven workflow lifecycle, MLFlow features, and the benefits of AI/ML product development and deployment. A general understanding of cloud architectures, software development principles CI/CD, deployment, APIs, microservices, Data and Dev ops, and Event-driven architectures is highly desirable.

The ability to deliver solutions that are based on a business understanding is essential.  Key responsibilities include enabling ML model and LLM-driven automation, and deployment to meet operational requirements like scalability, robustness, reusability, reproducibility, compliance, and responsible AI.  This position requires expertise and passion for working in agile teams to plan effectively, collaborating with broader cross-functional teams, and successfully deliver mission-critical Data and AI projects.  

The Senior Machine Learning Operations Engineer is the subject matter expert in MLOps and LLMOps. They have good understanding of tools and technologies used to manage AI and ML Model Life cycles. Provide technical support and coach junior ML Ops engineers.

Qualifications What makes you an ideal candidate? Proven experience in Azure ecosystem across the full AI/ML lifecycle with end-to-end automation (MLOps, LLMOPs), including ingestion and validation, dataset/version management, lineage and metadata tracking, feature engineering, experiment tracking, training, evaluation, pipeline-based validation gates, Responsible AI reviews, model registration, containerized deployment, event-driven monitoring, and integration like MLflow on Databricks or Azure Machine Learning Hands-on experience operationalizing GenAI solutions, including LLM application patterns such as Deep Research/RAG, prompt/version management, embedding workflows, model and prompt evaluation, safety/content filtering, human-in-the-loop review, and production monitoring (Tracing and Evaluations) for quality, latency, and cost Ability to understand and clearly articulate trade-offs of various approaches to solving AI and machine learning platforms e.g., Databricks vs.

Azure ML pipelines, batch vs. streaming, AKS vs. Managed Serving endpoints, cost vs. performance, governance vs. speed

Experience with event-driven architecture and streaming using Azure Event Hubs and Azure Function Apps Working knowledge in Azure Dev Ops or equivalent including Git Hub Repos, Boards, CI/CD and other related functionality

Experience delivering in agile environments (preferably SAFe) and partnering effectively with Data Science, Data Engineering, Application Engineering, Data Privacy and Security teams

Strong software engineering fundamentals (testing, code quality, packaging, reusable modules, infrastructure-as-code mindset, observability, and secure-by-default design)

Experience with large-scale data and lakehouse ecosystems, including Azure Databricks, Spark, and Delta-lake engines;
Strong quantitative, analytical and data interpretation skills with a solid foundation of mathematics, probability, and statistics

Ability to program in Python and SQL Experience with cloud deployment and runtime operations using Docker and Azure Kubernetes Service (AKS); familiarity with Git Ops and environment promotion patterns is a plus Experience building/hosting ML and GenAI services using Azure App Services and/or AKS (APIs, asynchronous workers, scheduled jobs), with attention to reliability and production readiness

Familiarity with the Azure AI ecosystem, including Azure AI Foundry, AI…