Senior Machine Learning Operations Engineer

GM Financial•Irving, TX

9h•Hybrid

About The Position

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 Lifecycles. Provide technical support and coach junior ML Ops engineers.

Requirements

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 DevOps or equivalent including GitHub 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 GitOps 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 Services, Azure ML, and enterprise governance for model access, secrets, and data protection
Strong applied ML understanding (e.g., regression, tree-based methods, neural networks, NLP) and the ability to translate business problems into measurable metrics and experiments
Experience with BI/analytics tools (especially Power BI) and awareness of Data and Analytics platforms such as Azure Databricks
Rapid 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
Experience 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
Strong communication skills with the ability to explain complex concepts to senior stakeholders and connect technical delivery to measurable business and financial outcomes
Proven ability to manage multiple workstreams in a time-sensitive environment and collaborate to solve problems creatively
Experience/certification in Azure, Azure AI, ML, Databricks, or equivalent such as AWS and GCP, MLOps, Data architecture, container orchestration, streaming, H2O, Spark, and Linux
Knowledge of Transactional, analytical databases and Data analysis techniques for structured, semi-structured, and unstructured data modeling and analysis (RDBMS, Columnar data-Parquet, JSON, etc.)
Broad knowledge of networking concepts including TCP/IP, subnetting, routing, DHCP, and others is desirable
Understanding of cybersecurity principles
4+ years as Data Scientist, Machine Learning Engineer, MLOps Engineer, AI Engineer or similar quantitative field required
High School Diploma or equivalent required

Nice To Haves

Azure Databricks experience preferred
Master’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required
PhD preferred

Responsibilities

Develop enterprise-wide and scalable cloud-based MLOps, LLMOps capabilities that span the full life cycle of Machine Learning, Deep Learning and Generative AI models
Develop 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
Continuously improve the speed, quality, and efficiency of model/experiments development, production, and maintenance
Collaborate with Model Management/Governance to develop and maintain enterprise-wide MLOps and LLMOps standards
Collaborate 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
Maintain 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
Develop and maintain a deep understanding of business requirements to ensure that MLOps and LLMOps solutions deliver practical and timely value
Conduct AI and ML Engineering research and proof of concept projects to improve practice and develop business cases that support business needs
Develop and apply algorithms that generate success metrics to improve the value of MLOps and LLMOps business function
Presents findings and analysis for use in decision making and demonstrate bottom-line financial benefits (ROI)
Collaborate with Business Teams, Data Architects, Data Engineers, Data Science and Cloud Solution Architects in developing solutions
Prioritizes tasks and meets project deadlines in a fast-paced work environment