À propos
Why GMF 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.
About this role:
You will design, build, and operate cloud-based MLOps capabilities that support the full lifecycle of analytical and generative AI models. This role blends machine learning engineering, data engineering, and software engineering, with a strong focus on automation, scalability, governance, and production readiness. You'll work with technologies such as MLflow, Databricks, Azure Machine Learning, CI/CD pipelines, containerization, and event-driven architectures, partnering closely with data science, IT, and business teams to deliver secure, compliant, and high-impact AI solutions.
Responsibilities
What makes you an ideal candidate: Build and maintain scalable, cloud-based MLOps platforms supporting analytical and GenAI models end to end Develop production-ready ML pipelines for training, deployment, monitoring, governance, and lifecycle automation Improve speed, quality, and reliability of model development, experimentation, and operations Partner with model governance, security, and compliance teams to define and enforce MLOps standards Collaborate with data science, engineering, and business stakeholders to deliver solutions aligned to business needs Research, prototype, and evolve MLOps capabilities to drive innovation and measurable business value Qualifications
Experience & Education
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 preferred,
PhD preferred Proven hands-on experience across the full ML/MLOps lifecycle, including MLflow and platforms such as Databricks, Azure ML, or SageMaker Experience operationalizing GenAI solutions, including LLM patterns (e.g., RAG), prompt/version management, evaluation, safety, and monitoring Strong software and cloud engineering fundamentals, including CI/CD, containerization (Docker), and Kubernetes (AKS) Experience with event-driven and streaming architectures and modern cloud-native design patterns Advanced skills with Python, SQL, and large-scale data platforms (e.g., Spark, Delta, lakehouse architectures) Ability to clearly communicate technical trade-offs and connect AI delivery to business and financial outcomes
What 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.
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 Irving, TX office.
This position is not open to agency submissions.
We are unable to provide visa sponsorship either now or in the future for this position.
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Compétences linguistiques
- English
Avis aux utilisateurs
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