Rackspace Technology · 15 hours ago

Senior MLOPs Engineer

United States

Full-time

Remote

Senior Level

$182K/yr - $228K/yr

6+ years exp

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Responsibilities

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

Qualification

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ML inference systemsDeep learning frameworksMachine learning algorithmsProgramming languagesApache Hadoop ecosystemPublic cloud servicesModel optimization techniques

Required

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.

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 8 years of relevant industry experience, or a Master's degree in Computer Science with at least 6 years of relevant industry experience.