Ml Ops Engineer

Job Description

ML OPS ENGINEER

Req #: 240127 Department: UW ADVANCEMENT: MRAI Job Location: Seattle Campus Job Location Detail: Hybrid Eligible - Minimum 2 days in office a week Posting Date: 10/17/2024 Closing Info: Open Until Filled Salary: $9,584 - $10,834 per month Other Compensation: Shift: First Shift Benefits: As a UW employee, you will enjoy generous benefits and work/life programs. For a complete description of our benefits for this position, please visit our website, click here. As a UW employee, you have a unique opportunity to change lives on our campuses, in our state and around the world. UW employees offer their boundless energy, creative problem-solving skills and dedication to build stronger minds and a healthier world.
UW faculty and staff also enjoy outstanding benefits, professional growth opportunities and unique resources in an environment noted for diversity, intellectual excitement, artistic pursuits and natural beauty.
For over 160 years, the University of Washington has been a hub for learning, innovation, problem solving and community building. Supporting this legacy and important public mission—with the goal of making the UW the world’s greatest public university, as measured by positive impact—guides everything we do in University Advancement . Bringing together development; alumni and stakeholder engagement; marketing and communications; and advancement operations, we advance the UW’s impact by developing meaningful connections that foster pride, advocacy and philanthropic support.
This starts with creating a culture of belonging within our organization that values the diverse experiences and expertise of our team members, allowing everyone to thrive and to contribute their unique talents as we strive to achieve our shared goals.
The Marketing, Research, Analytics and Insights team has an exciting opening for a Machine Learning Operations (ML Ops) Engineer. This is a full-time, permanent position.
Market Research, Analytics and Insights (MRAI) provides University Advancement and the broader University community with actionable insights derived from a variety of constituent data. By leveraging advanced analytics, artificial intelligence (AI), machine learning (ML) and research on donors, alumni, prospects and stakeholders across multiple engagement channels, MRAI helps to uncover new opportunities to strengthen connections, enhance institutional reputation, and secure critical resources that amplify the University’s impact.
The ML Ops Engineer will be pivotal in elevating the performance and quality of our Azure and Databricks environment, including developing and deploying machine learning models and take charge of our entire ML lifecycle using advanced tools, like Spark and MLflow. This position will create reusable code libraries, optimize models, and craft APIs for efficient model serving.
This position will act as a liaison and subject matter expert, collaborating closely with data scientists, IT professionals and various stakeholders to seamlessly integrate AI/ML components into our core business systems. This role will leverage machine learning, data science and cloud architecture in innovative projects — from conception to deployment — to ensure the delivery of high-quality market research, predictive modeling and performance monitoring.
What You Will Do:
Development and Design (70%)
•Develop, automate and manage robust CI/CD pipelines that efficiently collect, process and deliver data for production, analytics and ML processes, ensuring timely and accurate delivery.
•Leverage MLflow to track experiments, package code and manage model versions for reproducibility and scalability.
•Optimize models using Python and PySpark within the Azure Databricks environment to process and analyze complex datasets from diverse sources, ensuring robust and scalable solutions.
•Train, develop and deploy AI applications to automate processes, enhance decision-making and create dynamic user experiences.
•Guide system design, integrate AI/ML components into critical business systems and applications, and evaluate design trade-offs to optimize scalability, performance and maintainability within the MRAI framework.
•Work with vector databases and implement similarity search techniques to enhance data retrieval and analysis, ensuring systems can efficiently handle complex data queries and provide relevant results.
•Create and maintain reusable code libraries to support data scientists in ensuring consistency and efficiency in data science projects.
•Utilize GitHub for version control and project collaboration , ensuring code quality and consistency across the team.
Collaboration and Partnerships (15%)
•Collaborate with key technology teams, operating as a subject matter expert, to ensure the seamless integration of models into Advancement software and online services.
•Work closely with data scientists, IT professionals and other stakeholders to implement and maintain MLOps practices, including model versioning, performance monitoring and real-time updates.
•Work with ETL tools such as Apache Spark in the Databricks environment to facilitate data ingestion and transformation, enhancing collaboration with IT and Data Infrastructure staff.
•Present insights and share recommendations regarding Azure Databricks configuration with University Advancement’s Information Management partners to support MRAI projects and ensure reliability.
Continual Development and Industry Awareness (10%)
•Maintain expertise in cloud computing and big data technologies, particularly Azure and Databricks.
•Identify and evaluate new ML frameworks, tools and methodologies to assess their potential benefits and applicability to University Advancement objectives.
•Ensure the latest AI/ML, software development, and data science best practices are implemented within the team’s workflows and projects.
Other duties as assigned (5%)
MINIMUM REQUIREMENTS
Bachelor’s degree in computer science, data science, mathematics, engineering or a related field AND at least two years of experience in related roles, with at least one or more years of experience deploying and maintaining machine learning models in a production environment.
Equivalent education/experience will substitute for all minimum qualifications except when there are legal requirements, such as a license/certification/registration.
What You Bring:
·Proficiency in building, automating and managing data pipelines using Apache Spark within the Azure Databricks platform.
·Hands-on experience in deploying machine learning models and constructing complex data infrastructure specifically tailored for scalability and efficiency in Azure.
·Strong background working with cloud platforms paired with a solid understanding of MLOps tools and practices such as MLflow. This includes expertise in automating the ML lifecycle processes like model training, deployment, monitoring and maintenance.
·Extensive experience with machine learning techniques involving data preprocessing, training, evaluation and deployment of classification and regression models using Spark. Proficiency in programming languages critical for machine learning tasks, such as Python and Java.
·Expertise in system architecture focusing on performance and scalability within enterprise environments. Experience with major ML frameworks like TensorFlow, Keras and PyTorch is necessary for developing and optimizing diverse machine learning projects.
What You Can Expect:
·Cubicle/Open workspace environment which may result in additional or higher levels of noise and visual distractions.
·Ability to work evening and weekend hours, as necessary, on short or limited notice.
Application Process: The application process may include completion of a variety of online assessments to obtain additional information that will be used in the evaluation process. These assessments may include Work Authorization, Cover Letter and/or others. Any assessments that you need to complete will appear on your screen as soon as you select “Apply to this position”. Once you begin an assessment, it must be completed at that time; if you do not complete the assessment, you will be prompted to do so the next time you log into your “My Jobs” page. If you choose to take it later, it will appear on your "My Jobs" page to take when you are ready. Please note that your application will not be reviewed, and you will not be considered for this position until all required assessments have been completed.
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Committed to attracting and retaining a diverse staff, the University of Washington will honor your experiences, perspectives and unique identity. Together, our community strives to create and maintain working and learning environments that are inclusive, equitable and welcoming.

The University of Washington is an affirmative action and equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, age, protected veteran or disabled status, or genetic information.

To request disability accommodation in the application process, contact the Disability Services Office at 206-543-6450 or [email protected] .

Applicants considered for this position will be required to disclose if they are the subject of any substantiated findings or current investigations related to sexual misconduct at their current employment and past employment. Disclosure is required under Washington state law .

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