Senior Machine Learning Operations Engineer
General Motors8 hours ago
Irving, TX, USASenior
Responsibilities
- Develop enterprise-wide, scalable cloud-based MLOps and LLMOps capabilities spanning the full lifecycle of machine learning, deep learning, and generative AI models.
- Build reusable AI/ML pipelines for ingestion, validation, experimentation, training, deployment, monitoring, audit trails, lineage, metadata, model performance, data drift, and LLM tracing.
- Operationalize production-ready ML and GenAI services with automation, scalability, robustness, reproducibility, compliance, responsible AI, and cost efficiency.
- Collaborate with Model Management, Governance, data, cloud, cybersecurity, compliance, legal, vendors, and business stakeholders to establish standards and deliver solutions.
- Conduct AI/ML engineering research and proof-of-concept projects, develop success metrics, and communicate findings, ROI, and business value.
- Provide technical support and coach junior ML Ops engineers while managing multiple workstreams and meeting project deadlines.
Requirements
- At least 4 years of experience as a Data Scientist, Machine Learning Engineer, MLOps Engineer, AI Engineer, or in a similar quantitative field.
- Required high school diploma or equivalent and master's degree in computer science, engineering, analytics, mathematics, or a related discipline; PhD is preferred.
- Proven Azure ecosystem experience across the full AI/ML lifecycle, including automation, data and model management, experiment tracking, validation, deployment, monitoring, governance, and responsible AI.
- Hands-on experience operationalizing GenAI solutions using RAG or similar LLM application patterns, prompt and model evaluation, safety filtering, human review, tracing, and production monitoring.
- Experience with Azure Databricks, Spark, Delta Lake, Azure Machine Learning, MLflow, Python, SQL, Docker, Azure Kubernetes Service, APIs, and production service deployment.
- Experience with event-driven architecture and streaming using Azure Event Hubs and Azure Function Apps.
- Working knowledge of Azure DevOps or equivalent, GitHub repositories and boards, CI/CD, GitOps, and environment promotion patterns.
- Strong software engineering fundamentals including testing, code quality, packaging, reusable modules, infrastructure-as-code principles, observability, and secure-by-default design.
- Experience with large-scale data and lakehouse ecosystems, applied machine learning, quantitative analysis, probability, statistics, and structured, semi-structured, and unstructured data.
- Familiarity with Azure AI Foundry, Azure AI Services, Azure ML, enterprise governance, Power BI, Microsoft Copilot Studio, Databricks Genie, GitHub Copilot, AWS, GCP, H2O, container orchestration, Linux, and data architecture.
- Understanding of networking concepts and cybersecurity principles, plus strong communication, stakeholder collaboration, and business-oriented problem-solving skills.
Benefits
- Benefits are available on day one and include 401K matching, 12 weeks of fully paid bonding leave for new parents, tuition assistance, training, a GM employee auto discount, community service pay, and nine company holidays.
- Flexible hybrid work environment requiring two days per week in the office.
- Competitive pay and bonus eligibility are offered, with no specific compensation amount stated.
Tech Stack
Categories
About General Motors
General Motors designs, manufactures, and sells cars, trucks, and electric vehicles for consumers and commercial fleets under brands including Chevrolet, GMC, Cadillac, and Buick. A public company on the NYSE headquartered in Detroit and founded in 1908, it operates globally and is developing EVs on its Ultium battery platform. Revenue comes from vehicle and parts sales, connected services such as OnStar, and financing through GM Financial.