Principal MLOps Engineer

4 days ago

Canada Rackspace Full time

**About the Role:**

We are seeking a seasoned Principal ML OPS Engineer to lead the design, development, and optimization of our machine learning inference platform. The ideal candidate will have significant expertise in machine learning engineering and infrastructure, with a focus on building high-performance, cost-effective inference systems.

**Key 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.

**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.
  • Expert-level proficiency in at least one programming language such as Java, Python, or C++.
  • Ability to tackle complex challenges and devise effective solutions.
  • Worked effectively in a remote setting, maintaining strong written and verbal communication skills.
  • 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.
  • 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.

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