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Senior Machine Learning Operations Engineer
GM Financial. Develop enterprise-wide, scalable cloud-based MLOps and LLMOps capabilities spanning the full lifecycle of machine learning, deep learning, and generative AI models .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and operationalizing MLOps and LLMOps capabilities within the Azure ecosystem, ensuring production readiness and automation across the AI/ML lifecycle. Proficient in collaborating with cross-functional teams to deliver innovative AI solutions that meet business requirements and enhance model performance.
Highest-signal resume keywords
MLOps AutomationAzure Ecosystem ExperiencePython ProgrammingAI/ML Pipeline DevelopmentData Interpretation Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningGenerative AISQLDockerAzure Kubernetes ServiceAzure DatabricksEvent-Driven ArchitectureInfrastructure-as-CodeStatistical Analysis
Soft Skills
CollaborationAnalytical ThinkingProblem SolvingTime ManagementCommunication
Tools & Technologies
Azure DevOpsPower BIAzure AI ServicesGitHubAzure App Services
Certifications & Qualifications
Azure CertificationMLOps CertificationDatabricks Certification
Industry Keywords
Data ScienceCybersecurity PrinciplesAgile MethodologiesData GovernanceAI Engineering
Tech Stack
Tools & technologiesAWSAzureCloudCyber SecurityDockerGoogle Cloud PlatformKubernetesLinuxPythonSparkSQL
About the role
Key responsibilities & impact- Develop enterprise-wide, scalable cloud-based MLOps and LLMOps capabilities spanning the full lifecycle of machine learning, deep learning, and generative AI models
- Develop reusable, secure, and robust AI/ML pipelines
- Monitor model performance and data drift, use insights to train models, enable automatic audit trails, create LLM traces, and deploy across business applications
- Ensure AI/ML models are production ready and maintain automation across lifecycle activities
- Improve the speed, quality, and efficiency of model and experiment development, production, and maintenance
- Collaborate with Model Management/Governance to develop and maintain enterprise-wide MLOps and LLMOps standards
- Partner with internal stakeholders and vendors across Data Science, IT, Cybersecurity, Compliance, and Legal
- Maintain knowledge of advances in AI and ML Engineering and champion cost-effective, efficient, and innovative capabilities
- Understand business requirements and deliver practical, timely value
- Conduct AI/ML Engineering research and proof-of-concept projects
- Develop algorithms and success metrics to improve MLOps and LLMOps business value
- Present findings and analysis for decision-making and demonstrate financial benefits/ROI
- Collaborate with Business Teams, Data Architects, Data Engineers, Data Science, and Cloud Solution Architects
- Prioritize tasks and meet project deadlines in a fast-paced environment
Requirements
What you’ll need- 4+ years as Data Scientist, Machine Learning Engineer, MLOps Engineer, AI Engineer, or similar quantitative field
- Proven experience in the Azure ecosystem across the full AI/ML lifecycle with end-to-end MLOps and LLMOps automation
- Experience operationalizing GenAI solutions, including Deep Research/RAG, prompt/version management, embedding workflows, model and prompt evaluation, safety/content filtering, human-in-the-loop review, and production monitoring
- Experience with event-driven architecture and streaming using Azure Event Hubs and Azure Function Apps
- Working knowledge of Azure DevOps or equivalent, including GitHub Repos, Boards, and CI/CD
- Experience delivering in agile environments, preferably SAFe
- Strong software engineering fundamentals, including testing, code quality, packaging, reusable modules, infrastructure-as-code, observability, and secure-by-default design
- Experience with large-scale data and lakehouse ecosystems, including Azure Databricks, Spark, and Delta Lake
- Strong quantitative, analytical, and data interpretation skills with a foundation in mathematics, probability, and statistics
- Ability to program in Python and SQL
- Experience with Docker and Azure Kubernetes Service (AKS)
- Experience building or hosting ML and GenAI services using Azure App Services and/or AKS
- Familiarity with Azure AI Foundry, Azure AI Services, Azure ML, and enterprise governance
- Strong applied ML understanding, including regression, tree-based methods, neural networks, and NLP
- Experience with BI/analytics tools, especially Power BI
- Experience with low-code/no-code GenAI tools such as Microsoft Copilot Studio
- Experience with GenAI coding assistants such as Databricks Genie and GitHub Copilot
- Experience/certification in Azure, Azure AI, ML, Databricks, AWS, GCP, MLOps, data architecture, container orchestration, streaming, H2O, Spark, or Linux
- Knowledge of transactional and analytical databases and structured, semi-structured, and unstructured data modeling and analysis
- Understanding of cybersecurity principles
- High School Diploma or equivalent required
- Master’s Degree in Computer Science/Engineering, Analytics, Mathematics, or related discipline required
- Visa sponsorship is not available
Benefits
Comp & perks- 401K matching
- Bonding leave for new parents: 12 weeks, 100% paid
- Tuition assistance
- Training
- GM employee auto discount
- Community service pay
- Nine company holidays
- Bonus eligibility
- Flexible hybrid work environment