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GM Financial · 1 week ago

Machine Learning Operations Engineer II

Irving, TX

Full-time

Hybrid

Entry, Mid Level

2+ years exp

GM Financial is committed to AI-powered transformation and innovation within the financial sector. The Machine Learning Operations Engineer II will be responsible for designing, developing, and maintaining MLOps capabilities, ensuring that machine learning models are production-ready and scalable while collaborating with cross-functional teams to deliver impactful data and analytics projects.

Finance

Comp. & Benefits

No H1B

Responsibilities

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 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

Collaborating with Cloud Solution Architects in developing solutions

Qualification

MLOps lifecycle automationAzure ecosystem experiencePython programmingData engineering practicesMachine learning methodologiesSQL programmingAgile delivery methodsCloud architecture knowledgeAnalytical skillsCuriosityDriveCommunication skillsCollaboration skills

Required

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 functionalities

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

JavaScript, and SAS

Ability to design and implement model documentation and monitor 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 preferred

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

Strong written and verbal presentation skills with an ability to communicate effectively with Senior Management by making complex concepts easy to understand

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