Senior Machine Learning Engineer

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.

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.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Economics, or a related field.
• Minimum of 3 years of experience in machine learning engineering with a focus on end-to-end model development and deployment.
• 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/Skills:

• 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.

At McKesson, we care about the well-being of the patients and communities we serve, and that starts with caring for our people. That’s why we have a Total Rewards package that includes comprehensive benefits to support physical, mental, and financial well-being. Our Total Rewards offerings serve the different needs of our diverse employee population and ensure they are the healthiest versions of themselves.

As part of Total Rewards, we are proud to offer a competitive compensation package at McKesson. 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. 

Our Base Pay Range for this position