Sr. Machine Learning Engineer

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.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.

Job Title: Sr. Machine Learning Engineer

Current Need:

The Sr. Machine Learning Engineer will support the development of an Enterprise MLOps Platform, designed to support, and streamline machine learning projects across various business units. This role involves leading the integration and engineering of state-of-the-art machine learning models, including large language models (LLMs) and pretrained models for text, image, video, and audio data. The successful candidate will provide expert consulting to business units, helping to deploy scalable and efficient AI solutions that drive significant business value.

Key Job Responsibilities:

• Develop, train, and deploy machine learning models in Databricks/Azure Cloud, focusing on delivering measurable outcomes and improvements.
• Work closely with data scientists to understand domain-specific requirements and translate these into effective machine learning solutions.
• Implement end-to-end automation pipelines for data processing, model training, model validation, and deployment in collaboration with Data Science teams across the enterprise.
• Ensure that all machine learning solutions adhere to company-wide standards for scalability, reliability, and performance.
• Participate in cross-functional ML/DS team discussions to share insights, learn about best practices, and discuss new machine learning operational technologies and methodologies.
• Maintain up-to-date knowledge of the industry trends and advancements in machine learning and artificial intelligence.
• Provide ongoing maintenance and updates to existing models, ensuring they adapt to changing conditions and data.
• Document all processes and model configurations, ensuring transparency and reproducibility of results across teams.

Must have skills:

• Minimum of 3 years of experience in machine learning engineering with a focus on end-to-end model development and deployment.
• Hands-on experience in Terraform
• Strong in Azure cloud computing
• Proficient in Python and machine learning libraries and frameworks (e.g., TensorFlow, PyTorch).
• Experienced and skilled with ML services offered in Azure/Databricks.
• Demonstrated experience with MLOps tools (e.g., MLflow, Kubeflow, TFX) and cloud services (AWS, GCP, Azure) to deploy scalable solutions.
• Familiarity with LLMs such as OpenAI's GPT-3 or similar models. Experience in fine-tuning and deploying LLMs for various NLP tasks, including text generation, translation, summarization, sentiment analysis, etc.
• Strong understanding of statistical modeling and advanced analytics techniques.
• Excellent collaborative and communication skills, capable of working effectively across different teams and disciplines.
• Experience in a business-focused role, with a proven track record of applying machine learning to solve real-world business problems.

Preferred Qualifications:

• Machine Learning Expertise: Strong understanding of ML algorithms, statistical modeling, and supervised/unsupervised learning techniques. Familiarity with popular ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Azure Machine Learning: Proficiency in working with Azure Machine Learning services, including experience in deploying, managing, and monitoring ML models on Azure. Understanding of Azure ML pipelines, AutoML, and the Azure ML SDK.
• Azure Cloud Development: Proficiency in developing cloud-native applications on the Azure platform. Experience in leveraging Azure services like Azure Functions, Azure Logic Apps, Azure Event Grid, Azure Service Bus, and Azure Storage to build scalable and resilient cloud solutions. Understanding of Azure Resource Manager (ARM) templates for infrastructure provisioning and management. Familiarity with Azure DevOps for CI/CD pipelines and automated deployments.
• Programming Languages: Strong programming skills in languages commonly used in ML, such as Python and/or R. Ability to write efficient and scalable code. Understanding of software engineering best practices, version control systems (e.g., Git), and familiarity with containerization technologies like Docker.
• Data Engineering: Proficiency in data preprocessing, data cleaning, and feature engineering. Experience in working with large datasets and implementing data pipelines. Knowledge of SQL and NoSQL databases, as well as big data processing frameworks like Apache Spark.
• Cloud Computing: Familiarity with cloud platforms, particularly Microsoft Azure. Knowledge of Azure services like Azure Blob Storage, Azure Functions, Azure Data Factory, and Azure DevOps would be advantageous
• Model Deployment and Monitoring: Experience in deploying ML models in production environments, including knowledge of containerization, REST APIs, and microservices architecture. Ability to monitor model performance, diagnose issues, and implement necessary improvements.
• Collaboration and Communication: Strong teamwork and communication skills to collaborate effectively with cross-functional teams, including data scientists, software engineers, and stakeholders. Ability to explain complex ML concepts to non-technical stakeholders.
• Continuous Learning: Demonstrated passion for staying up-to-date with the latest advancements in ML, attending conferences, participating in online courses, and contributing to the ML community through publications or open-source projects.
• Domain Knowledge: Understanding of the specific domain or industry where ML solutions will be deployed, such as healthcare, finance, retail, etc. This knowledge will enable you to identify relevant ML use cases, design appropriate models, and solve domain-specific challenges.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Economics, or a related field.

Physical Requirements: General Office Demands

Relocation is NOT budgeted for this position.

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.

Our Base Pay Range for this position

McKesson is an Equal Opportunity Employer

McKesson provides equal employment opportunities to applicants and employees and is committed to a diverse and inclusive environment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age or genetic information. For additional information on McKesson’s full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page.

Join us at McKesson!