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Senior Machine Learning Operations Engineer

4 weeks ago

Irving, Texas, United States McKesson Full time
About the Role

Mckesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.

As a Senior Machine Learning Operations Engineer, you will be part of a lean software team dedicated to productionizing machine learning applications and systems at scale. You will participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.

You will focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You will have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

Key Responsibilities

  • Implement scalable and reliable systems leveraging cloud-based architectures, technologies, and platforms to handle model inference at scale.
  • Deploy and manage machine learning & data pipelines in production environments.
  • Work on containerization and orchestration solutions for model deployment.
  • Participate in fast iteration cycles, adapting to evolving project requirements.
  • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
  • Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
  • Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting, and automated model deployment.
  • Manage and monitor machine learning infrastructure, ensuring high availability and performance.
  • Implement robust monitoring and logging solutions for tracking model performance and system health.
  • Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
  • Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
  • Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
  • Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.
  • Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

Requirements

  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
  • Typically requires 7+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.
  • At least 3 years of experience designing and building data-intensive solutions using distributed computing.
  • At least 3 years of experience productionizing, monitoring, and maintaining models.
  • Must have skills:
    • Understanding of Azure stack like Azure Machine Learning, Azure Data Factory, Azure Databricks, Azure Kubernetes Service, Azure Monitor, etc.
    • Demonstrated expertise in building and deploying AI/Machine Learning solutions at scale leveraging cloud such as AWS, Azure, or Google Cloud Platform.
    • Experience in developing and maintaining APIs (e.g. REST).
    • Experience specifying infrastructure and Infrastructure as a code (e.g. Ansible, Terraform).
    • Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
    • Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g. Python, Spark, DataBricks, Github, MLFlow, Airflow).
    • Expertise in Unix Shell scripting and dependency-driven job schedulers.
    • Understanding of security and compliance requirements in ML infrastructure.
    • Experience with visualization technologies (e.g. RShiny, Streamlit, Python DASH, Tableau, PowerBI).
    • Familiarity with data privacy standards, methodologies, and best practices.