Senior MLOPs Engineer - GCP - (US Remote)

About the Role: 100% REMOTE!!!

We are looking for a seasoned Machine Learning Operations (MLOPs) Engineer to build, and optimize ML inference platform. The role demands an individual with significant expertise in Machine Learning engineering and infrastructure, with an emphasis on building Machine Learning inference systems. Proven experience in building and scaling ML inference platforms in a production environment is crucial. This remote position calls for exceptional communication skills and a knack for independently tackling complex challenges with innovative solutions.

Work Location: Remote

• Architect and optimize ML Platforms to support cutting-edge machine learning and deep learning models.
• Collaborate closely with cross-functional teams to translate business objectives into scalable engineering solutions.
• Lead the end-to-end development and operation of high-performance, cost-effective inference systems for a diverse range of models, including state-of-the-art large language models (LLMs).
• Provide technical leadership and mentorship to cultivate a high-performing engineering team.
• Develop CI/CD workflows for ML models and data pipelines using tools like Cloud Build, GitHub Actions, or Jenkins.
• Automate model training, validation, and deployment across development, staging, and production environments.
• Monitor and maintain ML models in production using Vertex AI Model Monitoring, logging (Cloud Logging), and performance metrics.
• Ensure reproducibility and traceability of experiments using ML metadata tracking tools like Vertex AI Experiments or MLflow.
• Manage model versioning and rollbacks using Vertex AI Model Registry or custom model management solutions.
• Collaborate with data scientists and software engineers to translate model requirements into robust and scalable ML systems.
• Optimize model inference infrastructure for latency, throughput, and cost efficiency using GCP services such as Cloud Run, Kubernetes Engine (GKE), or custom serving frameworks.
• Implement data and model governance policies, including auditability, security, and access control using IAM and Cloud DLP.
• Stay current with evolving GCP MLOps practices, tools, and frameworks to continuously improve system reliability and automation.

• Technical degree: Bachelor's degree in Computer Science with a minimum of 6 + years of relevant industry experience
• A Master's degree in Computer Science with at least 4 + years of relevant industry experience. Proven experience in implementing MLOps solutions onGoogle Cloud Platform (GCP) using services such asVertex AI,Cloud Storage,BigQuery,Cloud Functions, andDataflow.
• Proven experience in building and scalingagentic AI systems in production environments.
• Hands-on experience with leading deep learning frameworks such as TensorFlow, Pytorch, HuggingFace, Langchain, etc.
• Solid foundation in machine learning algorithms, natural language processing, and statistical modeling.
• Strong grasp of fundamental computer science concepts including algorithms, distributed systems, data structures, and database management.
• Ability to tackle complex challenges and devise effective solutions. Use critical thinking to approach problems from various angles and propose innovative solutions.
• Worked effectively in a remote setting, maintaining strong written and verbal communication skills. Collaborate with team members and stakeholders, ensuring clear understanding of technical requirements and project goals.

• Travel as per business requirements

• Candidate must be legally able to work for any employer in the US
• This role is not sponsorship eligible

The following information is required by pay transparency legislation in the following states: CA, CO, HI, NY, and WA. This information applies only to individuals working in these states.

· The anticipated starting pay range for Colorado is: $128,300 - 227,600

· The anticipated starting pay range for the states of Hawaii and New York (not including NYC) is: $136,600 - 249,100

· The anticipated starting pay range for California, New York City and Washington is: $149,500 - 273,100

Unless already included in the posted pay range and based on eligibility, the role may include variable compensation in the form of bonus, commissions, or other discretionary payments. These discretionary payments are based on company and/or individual performance and may change at any time. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. Information onbenefits offered is here.

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About Rackspace Technology

We are the multicloud solutions experts. We combine our expertise with the world’s leading technologies — across applications, data and security — to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future. Named a best place to work, year after year according to Fortune, Forbes and Glassdoor, we attract and develop world-class talent. Join us on our mission to embrace technology, empower customers and deliver the future.

More on Rackspace Technology

Though we’re all different, Rackers thrive through our connection to a central goal: to be a valued member of a winning team on an inspiring mission. We bring our whole selves to work every day. And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe. We welcome you to apply today and want you to know that we are committed to offering equal employment opportunity without regard to age, color, disability, gender reassignment or identity or expression, genetic information, marital or civil partner status, pregnancy or maternity status, military or veteran status, nationality, ethnic or national origin, race, religion or belief, sexual orientation, or any legally protected characteristic. If you have a disability or special need that requires accommodation, please let us know.