Senior Principal Engineer | Machine Learning Operations (MLOps)

Job Description

Job Description :

We are seeking a dedicated Machine Learning Engineer with a passion for data science, to lead and manage our critical Machine Learning and Artificial Intelligence Platform.

As part of the Data & Software Engineering (DSE) Team, you will be responsible for developing and maintain the infrastructure and tools required for the deployment, monitoring, and continuous improvement of machine learning models.

You will work closely with data scientists and software engineers to scale the Vertex Data Platform, which is Vertex’s cutting edge technology ecosystem for Data Engineering, Data Science, and Advanced Analytics.

Using the VDP, you will help ensure the seamless development, operations, and monitoring experience for data scientists, software engineers, and end users leveraging ML / AI models.

You will help Vertex scale and support ML / AI across our organization to fuel insights & innovation. You will partner with data engineers, data scientists, and operations teams to train, build, deploy, manage and scale ML models helping provide teams with best practices, guidance, and support.

Vertex Pharmaceuticals is in a transformational period where we are accelerating our capabilities, technologies, and data to augment our scientific mission, enable Vertex to grow in scale, and continue to be on the forefront of science, medicine, and technology.

As part of this effort, it is critical to ensure that the Vertex’s machine learning platform continues to accelerate and enable our data scientists to drive innovation and insight.

Key Responsibilities :

• Platform management - Manage, maintain, and improve Vertex’s ML platform with a focus on security, DevOps, and enabling best practices for users to design, implement, and manage end-to-end ML pipelines
• Technology expert Accountable for developing, deploying, & maintaining Vertex Data Platform components that enable & support the full development and deployment cycle for advanced Machine Learning and Artificial Intelligence solutions
• Delivery management Estimate, architect, and execute on delivery of critical projects & solutions, in partnership with the DSE leadership team, that support and enable data scientists
• Operations Management Manage & partner with a team of MLOps & Data Engineers to maintain the Vertex Data Platform. Troubleshoot and resolve issues related to model deployment, execution, and performance
• Innovation champion - Advocate for process enhancements and opportunities to improve our ML & Data Science capabilities.

Partner closely with Director, Data Platforms for defining, architecture and designing AI / ML platform components. Collaborate with data scientists and data engineers to integrate best practices into the development lifecycle

Qualifications :

• 8-10 years of experience working as a Software Engineer, DevOps Engineer, MLOps Engineer, or Machine Learning Engineer within a production ecosystem
• 3+ years working in an MLOps or ML Engineering role leveraging Databricks, AWS, or equivalent cloud data platforms
• Demonstrated experience with model pipeline technologies like Astronomer / Airflow, MLFlow, etc.
• Demonstrated experience in developing (or supporting) ML / AI solutions leveraging Python libraries such as PyTorch, Tensorflow, XGBoost, etc.
• Demonstrated experience monitoring and optimizing the security and performance of deployed models
• Experience automating model training, validation, and deployment processes to enhance efficiency and reproducibility
• Demonstrated experience working in agile environments (5+ years)
• Demonstrated ability to work independently and manage multiple projects that require collaboration across functional areas.
• Skillful, collaborative team player able to develop rapport and credibility with stakeholders.
• Demonstrated ability and willingness to teach, engage and support others as they learn new technologies and concepts.
• Enthusiasm for and the ability to quickly learn new technologies and tackle difficult problems.
• Strong presentation, verbal, and written communication skills
• Working knowledge of key workflow tools, including JIRA and Confluence

Flex Designation :

Hybrid-Eligible Or On-Site Eligible

Flex Eligibility Status :

In this Hybrid-Eligible role, you can choose to be designated as :

1. Hybrid : work remotely up to two days per week; or select

2. On-Site : work five days per week on-site with ad hoc flexibility.