{"id":2550779,"url":"https://alion.io/job/gm-financial-senior-machine-learning-operations-engineer","title":"Senior Machine Learning Operations Engineer","company":{"id":1900050,"name":"GM Financial","domain":"gmfinancial.com","url":"https://alion.io/company/gmfinancial-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":78,"open_postings":15,"ghost_share":0,"stale_share":0.867,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-14T06:34:16Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irving, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":124000,"max_usd":236000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1124},"experience_years_min":4,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Agile","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Azure AKS","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Copilot","optional":true},{"name":"Copilot Studio","optional":true},{"name":"Databricks","optional":true},{"name":"Delta Lake","optional":true},{"name":"DHCP","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"GitHub","optional":true},{"name":"GitOps","optional":true},{"name":"H2O.ai","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Kubernetes","optional":true},{"name":"Linux","optional":true},{"name":"LLMOps","optional":true},{"name":"NLP","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true},{"name":"RAG","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"TCP/IP","optional":true}],"status":"live","first_seen_at":"2026-10-14T15:49:08Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-14T21:34:40Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Why GMF Technology?\nInnovation 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.\nJoin us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.\nThis position will be posted until filled.\nAbout the Role\nApply 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 Lifecycles. Provide technical support and coach junior ML Ops engineers.\n In this role you will:\nDevelop enterprise-wide and scalable cloud-based MLOps, LLMOps capabilities that span the full life cycle of Machine Learning, Deep Learning and Generative AI models\nDevelop reusable, secure, and robust AI and ML pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails, LLM traces creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all AI/ML lifecycle activities. This includes developing code and making sure that AI/ML models are production ready\nContinuously improve the speed, quality, and efficiency of model/experiments development, production, and maintenance\nCollaborate with Model Management/Governance to develop and maintain enterprise-wide MLOps and LLMOps standards\nCollaborate with internal stakeholders and vendors in developing MLOps and LLMOps solutions that meet business requirements across a variety of areas including, but not limited to, Data Science, IT, Cybersecurity, Compliance, and Legal\nMaintain up to date knowledge about the latest advances in AI and ML Engineering, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative MLOps and LLMOps capabilities\nDevelop and maintain a deep understanding of business requirements to ensure that MLOps and LLMOps solutions deliver practical and timely value\nConduct AI and ML Engineering research and proof of concept projects to improve practice and develop business cases that support business needs\nDevelop and apply algorithms that generate success metrics to improve the value of MLOps and LLMOps business function\nPresents findings and analysis for use in decision making and demonstrate bottom-line financial benefits (ROI)\nCollaborate with Business Teams, Data Architects, Data Engineers, Data Science and Cloud Solution Architects in developing solutions\nPrioritizes tasks and meets project deadlines in a fast-paced work environment\n What makes you an ideal candidate?\nProven 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\nHands-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\nAbility 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\nExperience with event-driven architecture and streaming using Azure Event Hubs and Azure Function Apps\nWorking knowledge in Azure DevOps or equivalent including GitHub Repos, Boards, CI/CD and other related functionality\nExperience delivering in agile environments (preferably SAFe) and partnering effectively with Data Science, Data Engineering, Application Engineering, Data Privacy and Security teams\nStrong software engineering fundamentals (testing, code quality, packaging, reusable modules, infrastructure-as-code mindset, observability, and secure-by-default design)\nExperience 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\nAbility to program in Python and SQL\nExperience with cloud deployment and runtime operations using Docker and Azure Kubernetes Service (AKS); familiarity with GitOps and environment promotion patterns is a plus\nExperience 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\nFamiliarity with the Azure AI ecosystem, including Azure AI Foundry, AI Services, Azure ML, and enterprise governance for model access, secrets, and data protection\nStrong applied ML understanding (e.g., regression, tree-based methods, neural networks, NLP) and the ability to translate business problems into measurable metrics and experiments\nExperience with BI/analytics tools (especially Power BI) and awareness of Data and Analytics platforms such as Azure Databricks\nRapid prototyping and experimentation using low-code/no-code GenAI tools (e.g., Microsoft Copilot Studio) to quickly validate use cases, iterate on conversational flows, and transition successful prototypes into production-grade implementations\nExperience accelerating daily workflows with GenAI coding assistants (e.g., Databricks Genie, GitHub Copilot) while maintaining engineering rigor through code review, testing, security controls, and CI/CD\nStrong communication skills with the ability to explain complex concepts to senior stakeholders and connect technical delivery to measurable business and financial outcomes\nProven ability to manage multiple workstreams in a time-sensitive environment and collaborate to solve problems creatively\nExperience/certification in Azure, Azure AI, ML, Databricks, or equivalent such as AWS and GCP, MLOps, Data architecture, container orchestration, streaming, H2O, Spark, and Linux\nKnowledge of Transactional, analytical databases and Data analysis techniques for structured, semi-structured, and unstructured data modeling and analysis (RDBMS, Columnar data-Parquet, JSON, etc.) \nBroad knowledge of networking concepts including TCP/IP, subnetting, routing, DHCP, and others is desirable\nUnderstanding of cybersecurity principles\nWork Experience & Education\n4+ years as Data Scientist, Machine Learning Engineer, MLOps Engineer, AI Engineer or similar quantitative field required\nAzure Databricks experience preferred\nHigh School Diploma or equivalent required\nMaster’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required\nPhD preferred\nWhat We Offer: 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.\nOur Culture: Our team members define and shape our culture - an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work - we thrive.\nCompensation: Competitive pay and bonus eligibility.\nWork Life Balance: Flexible hybrid work environment, 2-days a week in office.\nNOTE: We are unable to consider candidates who require visa sponsorship for this position\nThis position is not open to agency submissions\n#GMFJobs #LI-Hybrid #LI-MH1","description_format":"text","description_chars":9212,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"high_school","optional":true},"security_clearance":false,"languages":[]},"benefits":["401k plan","Hybrid work"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Consumer Loans & Pawnshops","Leasing, Auto & Equipment Finance"],"lifecycle":[{"event":"open","at":"2026-10-14T17:54:45Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":44,"reasons":["conf:1","win:early"],"computed_at":"2026-10-14T22:40:39Z"},"pay":null,"html_url":"https://alion.io/job/gm-financial-senior-machine-learning-operations-engineer","json_url":"https://alion.io/job/gm-financial-senior-machine-learning-operations-engineer.json","meta":{"generated_at":"2026-10-14T22:40:39Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":30608,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-15T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1900050},"rest":"https://alion.io/mcp/rest/get_company?id=1900050"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fgm-financial-senior-machine-learning-operations-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fgm-financial-senior-machine-learning-operations-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fgm-financial-senior-machine-learning-operations-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/gm-financial-senior-machine-learning-operations-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fgm-financial-senior-machine-learning-operations-engineer"}]}