Senior Machine Learning Operations Engineer

GM Financial•Irving, TX

1d•Hybrid

About The Position

The Senior Machine Learning Operations Engineer is a subject matter expert in MLOps and LLMOps who applies AI/ML Engineering, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain machine learning and LLM operations capabilities. The role supports the full lifecycle of Machine Learning, Deep Learning, and Generative AI models, with a focus on scalable, robust, reusable, reproducible, compliant, and responsible AI solutions. It requires understanding of model development and GenAI/LLM workflows, MLflow, cloud architectures, software development, CI/CD, APIs, microservices, DataOps, DevOps, and event-driven architectures. The engineer provides technical support and coaching to junior MLOps engineers and works in agile teams with cross-functional partners to deliver mission-critical data and AI projects. The position develops enterprise-wide cloud-based MLOps and LLMOps capabilities and production-ready automation, pipelines, model monitoring, data-drift monitoring, audit trails, LLM traces, and deployment solutions. It collaborates with Model Management/Governance, business and technical stakeholders, and vendors to maintain standards and meet business requirements. The role also conducts research and proof-of-concept projects, develops success metrics and business cases, presents findings and ROI, and continually improves the speed, quality, efficiency, and cost-effectiveness of model development and operations. The role requires proven experience across the Azure AI/ML lifecycle, including operationalizing GenAI applications and managing production monitoring. It involves helping translate business needs into measurable technical outcomes and communicating complex concepts and financial value to senior stakeholders. Work is performed in a flexible hybrid environment, with two days per week in the office. The position will be posted until filled. Candidates requiring visa sponsorship cannot be considered.

Requirements

At least 4 years of experience as a Data Scientist, Machine Learning Engineer, MLOps Engineer, AI Engineer, or in a similar quantitative field.
High school diploma or equivalent.
Master’s degree in Computer Science, Engineering, Analytics, Mathematics, or a related discipline.
Proven Azure ecosystem experience across the full AI/ML lifecycle with end-to-end automation, including ingestion and validation, dataset/version management, lineage and metadata tracking, feature engineering, experiment tracking, training, evaluation, pipeline validation gates, Responsible AI reviews, model registration, containerized deployment, event-driven monitoring, and integration such as MLflow on Databricks or Azure Machine Learning.
Hands-on experience operationalizing GenAI solutions, including Deep Research/RAG patterns, prompt and version management, embedding workflows, model and prompt evaluation, safety/content filtering, human-in-the-loop review, and production tracing and evaluation for quality, latency, and cost.
Ability to understand and clearly explain trade-offs among AI/ML platform approaches, including Databricks versus Azure ML pipelines, batch versus streaming, AKS versus managed serving endpoints, cost versus performance, and governance versus speed.
Experience with event-driven architecture and streaming using Azure Event Hubs and Azure Function Apps.
Working knowledge of Azure DevOps or equivalent tools and functionality, including GitHub Repos, Boards, and CI/CD.
Experience delivering in agile environments, preferably SAFe, and partnering with Data Science, Data Engineering, Application Engineering, Data Privacy, and Security teams.
Strong software engineering fundamentals, including testing, code quality, packaging, reusable modules, infrastructure-as-code practices, 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 foundation in 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).
Experience building or hosting ML and GenAI services using Azure App Services and/or AKS, including APIs, asynchronous workers, and scheduled jobs, with attention to reliability and production readiness.
Familiarity with the Azure AI ecosystem, including Azure AI Foundry, AI Services, Azure ML, and enterprise governance for model access, secrets, and data protection.
Strong applied ML understanding, including regression, tree-based methods, neural networks, and NLP, and ability to translate business problems into measurable metrics and experiments.
Strong communication skills, including explaining complex concepts to senior stakeholders and connecting technical delivery to measurable business and financial outcomes.
Ability to manage multiple workstreams in a time-sensitive environment and collaborate creatively to solve problems.
Knowledge of transactional and analytical databases and data analysis techniques for structured, semi-structured, and unstructured data modeling and analysis, including RDBMS, Parquet, and JSON.
Understanding of cybersecurity principles.

Nice To Haves

Azure Databricks experience.
Experience with GitOps and environment promotion patterns.
Experience with BI/analytics tools, especially Power BI, and awareness of data and analytics platforms such as Azure Databricks.
Experience with rapid prototyping using low-code/no-code GenAI tools such as Microsoft Copilot Studio, including validating use cases and moving successful prototypes into production-grade implementations.
Experience accelerating workflows with GenAI coding assistants such as Databricks Genie and GitHub Copilot while maintaining code review, testing, security, and CI/CD rigor.
Experience or certification in Azure, Azure AI, ML, Databricks, or equivalent platforms such as AWS and GCP, MLOps, data architecture, container orchestration, streaming, H2O, Spark, and Linux.
Broad knowledge of networking concepts such as TCP/IP, subnetting, routing, and DHCP.
PhD.

Responsibilities

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, robust AI/ML pipelines; monitor model performance and data drift; use insights to train models; enable audit trails and LLM traces; deploy models across business applications; and automate lifecycle activities.
Develop code and ensure AI/ML models are production-ready.
Continuously improve the speed, quality, and efficiency of model and experiment development, production, and maintenance.
Collaborate with Model Management and Governance to develop and maintain enterprise-wide MLOps and LLMOps standards.
Work with internal stakeholders and vendors, including Data Science, IT, Cybersecurity, Compliance, and Legal, to develop solutions that meet business requirements.
Maintain knowledge of advances in AI and ML Engineering and champion cost-effective, efficient, and innovative MLOps and LLMOps capabilities.
Develop and maintain an understanding of business requirements to ensure solutions deliver practical and timely value.
Conduct AI and ML Engineering research and proof-of-concept projects and develop business cases supporting business needs.
Develop and apply algorithms that generate success metrics to improve the MLOps and LLMOps business function.
Present findings and analysis for decision-making and demonstrate financial benefits and ROI.
Collaborate with Business Teams, Data Architects, Data Engineers, Data Science, and Cloud Solution Architects to develop solutions.
Prioritize tasks and meet project deadlines in a fast-paced work environment.
Provide technical support and coach junior ML Ops engineers.