What are the responsibilities and job description for the Machine Learning Operations-Engineer II position at GM Financial?
Job Description
Sponsorship Notice: At this time, we are unable to offer employment sponsorship for this position. This includes, but is not limited to, H-1B, TN, L1, and OPT visa types.
Why GM Financial Technology
Innovation 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.
Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.
Responsibilities
About the Role:
Apply ML pipelines, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain MLOps/LLMOps/GenAIOps capabilities. This position requires an understanding of the Data Science model development lifecycle, MLFlow architecture, and the benefits of ML development automation and deployment. A general understanding of cloud architectures, software development principles CI/CD, deployment, APIs, microservices, data dev ops, and event-driven cloud architectures is highly desirable. The ability to deliver solutions that are based on a business understanding is essential. Key responsibilities include enabling ML/GenAI model automation, deployment, scalability, management, 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 analytics projects.
Education and Experience:
Our 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.
Compensation: Competitive pay and bonus eligibility
Work Life Balance: Flexible hybrid work environment, 2-days a week in office
#GMFjobs
Sponsorship Notice: At this time, we are unable to offer employment sponsorship for this position. This includes, but is not limited to, H-1B, TN, L1, and OPT visa types.
Why GM Financial Technology
Innovation 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.
Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.
Responsibilities
About the Role:
Apply ML pipelines, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain MLOps/LLMOps/GenAIOps capabilities. This position requires an understanding of the Data Science model development lifecycle, MLFlow architecture, and the benefits of ML development automation and deployment. A general understanding of cloud architectures, software development principles CI/CD, deployment, APIs, microservices, data dev ops, and event-driven cloud architectures is highly desirable. The ability to deliver solutions that are based on a business understanding is essential. Key responsibilities include enabling ML/GenAI model automation, deployment, scalability, management, 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 analytics projects.
- Develop enterprise-wide and scalable cloud-based MLOps, LLMOps, GenAIOps capabilities that span the full lifecycle of analytical models
- Develop reusable, secure, and robust ML/LLM/GenAI pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all ML life cycle activities. This includes developing code and making sure that ML/LLM/GenAI models are production ready
- Continuously improve the speed, quality, and efficiency of model/experiments development, deployment, and maintenance
- Collaborate with Model Management/Governance to develop and maintain enterprise wide MLOps standards
- Collaborate with internal stakeholders and vendors in developing MLOps 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 MLOps, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative capabilities
- Develop and maintain a deep understanding of business requirements to ensure that MLOps solutions deliver practical and timely value
- Conduct MLOps research and proof of concept projects to improve practice and develop business cases that support business needs
- Develops and apply algorithms that generate success metrics to improve the value of models/experiments.
- Presents findings and analysis for use in decision making and demonstrate bottom-line financial benefits
- Collaborate with Cloud Solution Architects in developing solutions
- Prioritizes tasks and meets project deadlines in a fast-paced work environment
- Studies and/or experience in full ML/LLM/GenAI lifecycle automation that includes data ingestion, data validation, data and source versioning, attribute lineage, feature engineering, evaluation of model experiments, model training, model validation in release pipelines, assessing responsible AI, model registration, containerized deployment, event-driven monitoring, and integration with ML Flow pipelines
- Ability to understand and clearly articulate trade-offs of various approaches to solving machine learning platform problems
- Experience with messaging technologies such as Azure Event Hubs and Azure Event Grid is highly desirable
- Working knowledge in Azure DevOps or equivalent including GitHub, Boards, CI/CD and other related functionality
- Experience in agile delivery methods like Scrum/Kanban frameworks.
- Broad knowledge in software engineering principles
- Working experience with large data sets
- Strong quantitative, analytical and data interpretation skills with a solid foundation of mathematics, probability, and statistics
- Ability to identify and understand business issues and map these issues into operational and quantitative questions
- Demonstrated understanding of applied analytical methodologies including Decision Trees, Neural Networks, Regression, NLP, chat bots and other AI methodologies
- Ability to program in Python and SQL
- Proficient in Excel, Word, and PowerPoint
- JavaScript, and SAS
- Ability to design and implement model documentation and monitoring protocols
- Knowledge of analytical databases and data analysis techniques
- Ability, drive, and curiosity to quickly learn and stay up to date on technological and analytical advances
- Experience/certification in the Azure ecosystem with particular emphasis on data and Machine Learning
- Experience/certification in Azure, Azure ML, Databricks, or equivalent such as AWS and GCP, MLOps, data architectures, container orchestration, streaming, H2O, Spark, and Linux
- Ability to program in Python and SQL. Experience in other languages such as Java, C#, and JavaScript to extend Azure capabilities is highly desirable
- Experience in business intelligence tools like Azure Synapse, Snowflake, and similar technology
- Experience in PowerBI is desirable
- Understanding of steps involved in developing and deploying ML models in management platforms, like MLFlow, Azure ML, etc.
- Knowledge of working with Docker containers and Kubernetes
- Knowledge of frameworks such as Keras, Pytorch and TensorFlow
- Knowledge of structured, semi-structured, and unstructured data modeling and analysis (RDBMS, Columnar data, JSON, etc.)
- Broad knowledge of networking concepts including TCP/IP, subnetting, routing, DHCP, and others is desirable
- Understanding of cybersecurity principles
- Ability, drive, and curiosity to learn how the business works and develop a deep understanding of business needs
- Ability to quickly learn new technologies and develop practical solutions
- Ability to solve problems creatively and collaboratively
- Strong written and verbal presentation skills with an ability to communicate effectively with Senior Management by making complex concepts easy to understand
- Translating technical efforts into financial benefits
- Capable of managing multiple and varied projects, including the ability to coordinate and balance numerous tasks in a time-sensitive environment, under pressure
Education and Experience:
- 2-4 years as Data Scientist or machine learning engineer or similar quantitative field required
- High School Diploma or equivalent required
- Master’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required
- PhD preferred
Our 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.
Compensation: Competitive pay and bonus eligibility
Work Life Balance: Flexible hybrid work environment, 2-days a week in office
#GMFjobs