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Principal MLOPs Engineer

About the Role:


We are looking for a seasoned Principal ML OPS Engineer to architect, 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.


What you will be doing:
  • Architect and optimize our existing data infrastructure to support cutting-edge machine learning and deep learning models.
  • Collaborate closely with cross-functional teams to translate business objectives into robust engineering solutions.
  • Own 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 LLMs.
  • Provide technical leadership and mentorship to foster a high-performing engineering team.


Requirements:
  • Proven track record in designing and implementing cost-effective and scalable ML inference systems. 
  • Hands-on experience with leading deep learning frameworks such as TensorFlow, Keras, or Spark MLlib. 
  • 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.
  • Proven experience in Apache Hadoop ecosystem (Oozie, Pig, Hive, Map Reduce).
  • Expertise in public cloud services, particularly in GCP and Vertex AI.


Must have:
  • Proven expertise in applying model optimization techniques (distillation, quantization, hardware acceleration) to production environments.
  • Proficiency and recent experience in Java is required (Must have)
  • In-depth understanding of LLM architectures, parameter scaling, and deployment trade-offs.
  • Technical degree: Bachelor's degree in Computer Science with a minimum of 10+ years of relevant industry experience, or
  • A Master's degree in Computer Science with at least 8+ years of relevant industry experience.
  • A specialization in Machine Learning is preferred. 


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: $204,000 - $255,00

·       The anticipated starting pay range for the states of Hawaii and New York (not including NYC) is: $191,600 - 239,500

·       The anticipated starting pay range for California, New York City and Washington is: $223,200 - 279,000

 

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 on benefits offered is here.



#LI-VM1

#Rackspace

#LI-Rackspace

#LI-USA

#LI-Remote



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.

 

 


Average salary estimate

$241500 / YEARLY (est.)
min
max
$204000K
$279000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Principal MLOPs Engineer, Rackspace

Are you ready to take your career to the next level? Rackspace Technology is on the lookout for a dynamic Principal MLOps Engineer to join our innovative team. In this key role, you will architect, build, and optimize our ML inference platform, playing a pivotal role in unlocking the full potential of machine learning technologies. Your extensive experience in machine learning engineering and infrastructure is invaluable as you transform complex challenges into innovative solutions. Collaborating with various cross-functional teams, you’ll translate business objectives into practical engineering applications that support cutting-edge models, including state-of-the-art LLMs. Your technical prowess will lead the charge in developing high-performance, efficient inference systems. We value strong communication skills and a proactive approach, especially in a remote working environment. You'll also provide mentorship and technical guidance to nurture our engineering talent, making a meaningful impact in shaping the future of our projects. Join us in our commitment to empower customers and embrace technology on our mission to deliver outstanding solutions.

Frequently Asked Questions (FAQs) for Principal MLOPs Engineer Role at Rackspace
What are the responsibilities of a Principal MLOps Engineer at Rackspace Technology?

As a Principal MLOps Engineer at Rackspace Technology, you will be responsible for architecting and optimizing our data infrastructure, collaborating with cross-functional teams, and owning the development of high-performance ML inference systems. This role requires you to leverage your expertise in machine learning technologies to deliver robust engineering solutions that align with our business objectives.

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What qualifications are needed to apply for the Principal MLOps Engineer position at Rackspace Technology?

To apply for the Principal MLOps Engineer role at Rackspace Technology, candidates should ideally hold a Bachelor's or Master's degree in Computer Science or a related field, coupled with at least 8-10 years of relevant industry experience. Hands-on proficiency in machine learning frameworks like TensorFlow and Keras, as well as deep familiarity with ML inference system design, is essential.

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How important is remote work experience for the Principal MLOps Engineer position at Rackspace Technology?

Remote work experience is highly valued for the Principal MLOps Engineer position at Rackspace Technology. Effective communication skills and the ability to collaborate with team members across distances are key to achieving success in this role, ensuring clarity in project goals and technical requirements.

Join Rise to see the full answer
What technologies should a qualified Principal MLOps Engineer at Rackspace Technology be familiar with?

A qualified Principal MLOps Engineer at Rackspace Technology should have a solid understanding of deep learning frameworks like TensorFlow and Keras. Familiarity with the Apache Hadoop ecosystem, cloud services (especially GCP and Vertex AI), and model optimization techniques are also crucial for this role.

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What is the anticipated salary range for the Principal MLOps Engineer role at Rackspace Technology?

The anticipated salary range for the Principal MLOps Engineer position at Rackspace Technology varies by state. In Colorado, it ranges from $204,000 to $255,000, while in California, New York, and Washington, it ranges from $223,200 to $279,000. Compensation may include additional variable payments based on performance.

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Common Interview Questions for Principal MLOPs Engineer
Can you explain your experience with ML inference systems?

When answering this question, highlight specific projects where you've designed and implemented ML inference systems, discussing the frameworks used and the outcomes achieved. Emphasize any quantitative results or improvements, as well as your approach to tackling challenges during these projects.

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What deep learning frameworks are you most familiar with, and how have you utilized them?

Discuss your hands-on experience with popular frameworks such as TensorFlow and Keras. Share examples of how you've applied these tools in your work, particularly in developing and optimizing machine learning models for production environments.

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Describe a complex problem you faced and how you solved it as an MLOps Engineer.

Outline a specific challenge you encountered, detailing the context and your thought process in approaching it. Focus on your critical thinking skills, the strategies you employed to devise an effective solution, and the results of your efforts.

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How do you ensure effective communication when working remotely?

Illustrate your communication strategies in a remote setting, including regular check-ins, the use of collaboration tools, and clear documentation. Share how these practices have led to successful project outcomes and smooth teamwork despite geographical distances.

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What is your approach to mentoring team members in a technical role?

Explain your mentoring philosophy, emphasizing support, feedback, and providing opportunities for growth. Describe how you’ve successfully mentored junior engineers in the past and the positive impact it had on the team's performance.

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Can you describe your experience with cloud services, particularly in GCP?

Discuss your expertise with GCP and its associated services, highlighting any projects where you've leveraged these tools. Focus on how you utilized GCP to enhance ML operations, data analysis, or model deployment.

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What considerations do you keep in mind when designing an ML inference system?

Mention key factors such as scalability, cost-effectiveness, performance efficiency, and model compatibility. Discuss how you balance these considerations to create a robust, high-performing ML inference system.

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What is your experience with model optimization techniques?

Explain any model optimization techniques you've applied, such as distillation, quantization, or hardware acceleration. Provide examples of how these techniques improved your ML models' performance or reduced resource consumption.

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How do you stay current with developments in machine learning and MLOps?

Focus on your methods for ongoing learning, such as attending workshops, following relevant industry publications, or participating in online courses. Mention any communities you engage with to remain up-to-date with the rapidly evolving field of machine learning.

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What do you foresee as the future challenges in MLOps?

Share your insights on upcoming trends and challenges in MLOps, such as data privacy, model interpretability, or the integration of AI and machine learning with other technological advancements. Discuss how you plan to address these challenges in your future roles.

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Founded in 1998, Rackspace provides multi-cloud computing solutions and services. Offering advising to customers based on business challenges, designing solutions, building, and managing solutions. The company is headquartered in San Antonio, Texa...

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Full-time, remote
DATE POSTED
March 26, 2025

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