MLOps Engineer (RARR Job 5731)
Job Skills
Job Description
As an ML Engineering Specialist, you will be responsible for operationalizing ML models including NLP, Computer Vision, and other types of models. You will manage the end-to-end lifecycle of machine learning models and collaborate closely with data scientists, engineers, and business stakeholders to deliver scalable and reliable ML solutions.
Key Responsibilities
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Own the responsibility to operationalize ML models across NLP, Computer Vision, and other domains.
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Manage the end-to-end model lifecycle, from feature extraction to model monitoring, using high-end tools and technologies.
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Design and implement DevOps principles in Machine Learning workflows.
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Ensure model quality assurance, governance, and monitoring.
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Integrate ML models into business applications via APIs.
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Execute best practices in version control and continuous integration/delivery.
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Collaborate with data scientists, engineers, and other key stakeholders.
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Work efficiently in a fast-paced, cross-functional environment.
Mandatory Skills & Requirements
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Experience implementing machine learning lifecycle on Azure ML and Azure Databricks, along with Azure services such as Azure DevOps, Azure Functions, etc.
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Experience with machine learning frameworks, libraries, and working in agile environments.
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Experience implementing Azure Cognitive Services in business applications.
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Experience with various model deployment strategies.
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Strong experience with Python and SQL; understanding of distributed frameworks like Spark, Dask, Ray, etc. is a plus.
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Experience with version-control tools such as Git, Bitbucket, etc.
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Knowledge of Docker, Jenkins, Kubernetes, and other DevOps tools.
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Knowledge of Infrastructure-as-Code using ARM or Terraform templates.
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Familiarity with Large Language Models (LLMs) and operationalizing foundation models on cloud platforms is a plus.
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Outstanding analytical and problem-solving skills.
