Principal MLOPs Engineer (Canada)

Rackspace Technology; Toronto; Remote; CAD 125,000 - 150,000; Full time

Job summary

A forward-thinking company is seeking a Principal ML OPS Engineer to architect and optimize machine learning inference platforms. This remote role offers the opportunity to work with cutting-edge technologies and collaborate with cross-functional teams to deliver robust engineering solutions. The ideal candidate will have a strong background in machine learning engineering, proven experience in building scalable inference systems, and exceptional communication skills. Join this innovative firm and contribute to the development of state-of-the-art models while providing technical leadership to a high-performing team. If you are passionate about tackling complex challenges and driving impactful solutions, this is the perfect opportunity for you.

Qualifications

• 10+ years in ML engineering with expertise in building inference systems.
• Hands-on experience with Java and deep learning frameworks.

Responsibilities

• Architect and optimize data infrastructure for ML and deep learning.
• Lead development and operation of high-performance inference systems.

Skills

Machine Learning Engineering

Communication Skills

Problem Solving

Java

Machine Learning Algorithms

Natural Language Processing

Statistical Modeling

Critical Thinking

Education

Bachelor's degree in Computer Science

Master's degree in Computer Science

Tools

TensorFlow

Keras

Spark MLlib

Apache Hadoop

GCP

Vertex AI

Job description

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.
• Proficiency and recent experience in Java is required (Must have).
• 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.