Principal Machine Learning Engineer

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The Principal Machine Learning Python Engineer will be responsible for developing our machine learning infrastructure and solutions cross functional teams, as well as building AI/ML operation components to help speed up our machine learning development lifecycle. This role will also work together with the entire data science team to support our model building efforts. The ideal candidate is a leader in cloud infrastructure and machine learning, ideally GCP and Vertex AI. This Principal Machine Learning Python Engineer role will involve leading the AI model development process in our AI Pillar

UKG works in an agile environment where there are daily stand-ups, code reviews, and constant communication within each self-managed cross-functional team. The ability to communicate effectively with Product Managers and Architects, as well as work closely with other team members are key components for success in this position. The candidate should be comfortable working in an agile/scrum environment, utilizing standardized frameworks, Git repositories, and CI/CD processes for deployment.

Responsibilities:
• Software Development: Write clean, maintainable, and efficient code or various software applications and systems. Design, develop, and maintain scalable machine learning and AI pipelines using modern cloud ML/AI applications such as Google Vertex AI, or Databricks MLFlow. Design and implement infrastructure to support the machine learning development lifecycle. Create solutions that empower our data scientist team members to rapidly prototype and develop machine learning/analytic solutions.
• Collaboration: with cross-functional teams, analysts, product owners, data engineers, data scientist and business users to understand business requirements and translate them into technical solutions. Work with data scientists and analytics experts to strive for greater functionality in our machine learning/analytic solutions.
• Continuous Learning: Continuously learn and apply new business and functional knowledge to improve Machine Learning Engineering/Ops practices.
• Governance: Ensure data quality, integrity, and security across all ML/AI pipelines, compute, VM and storage solutions.
• Technical Leadership: Lead the design, development, and deployment of complex software applications and systems, ensuring they meet high standards of quality and performance.
• Project Management: Manage execution and delivery of features and projects, negotiating project priorities and deadlines, ensuring successful and timely completion, with quality. Participate in agile/scrum ceremonies, contributing to sprint planning, stand-ups, and retrospectives.
• Architectural Design: Lead the design reviews with peers and stakeholders and in the architectural design of new features and systems, ensuring scalability, reliability, and maintainability.
• Code Review: Diligent about reviewing code developed by other developers, provide feedback and maintain a high bar of technical excellence to ensure code is adhering to industry standard best practices like coding guidelines, elegant, efficient and maintainable code, with observability built from ground up, unit tests etc.
• Testing: Build testable software, define tests, participate in the testing process, automate tests using, tools (e.g., Junit, Selenium) and Design Patterns leveraging the test automation pyramid as the guide.
• Service Health and Quality: Maintain the health and quality of services and incidents, proactively identifying and resolving issues. Utilize service health indicators and telemetry for action providing recommendations to optimize performance. Conduct thorough root cause analysis and drive the implementation of measures to prevent future recurrences.
• Dev Ops Model: Understanding of working in a DevOps Model. Taking ownership from working with product management on requirements to design, develop, test, deploy and maintain the software in production.
• Documentation: Properly document new features, enhancements or fixes to the product, contributing to training materials.
• Data Wrangling (Quality): Wrangle and integrate data from disparate systems to allow data analysts and scientists leverage for end-to-end data and information.

Required Qualifications
• Bachelors or Master's degree in Computer Science, Mathematics, Computational Linguistics or similar field.
• 7-10 years engaging and working with cross-functional enterprise level software development projects.
• Proven recent experience as Senior Python Engineer, Data Engineer, Full Stack Data Engineer, or similar role.
• Expert with testing tools such as PyTest, xUnit, mocking frameworks, etc.
• Expert with commonly used data science libraries such as Numpy, Pandas, Scikit-learn, Scipy, and Spacy,
• Expert using a deep learning framework such as PyTorch, Tensorflow, or Keras.
• Understanding of machine learning algorithms such as Linear and Logistic regressions, Decision tree, Naive Bayes, KNN, K-means, and Random forest.
• Expert with GCP technologies such as BigQuery, GKE, GCS, DataFlow, Kubeflow, and/or VertexAI.
• Expert building and optimizing ML pipelines, preferably in Kubeflow.
• Advanced working SQL knowledge and experience working with relational databases, as well as working familiarity with a variety of databases, preferably BigQuery, MySql, and MongoDB.
• Strong understanding of computer science fundamentals, including algorithms, data structures, and software design principles.
• Proficiency with Git as a version control system.
• Experience with Docker and Kubernetes.
• Familiarity with tools used to automate the deployment of an enterprise software solution to the cloud, Terraform, GitHub Actions, Concourse, Ansible, etc.
• Experience with, or understanding of, Agile/Lean Software Development practices such as Kanban or Scrum.
• Excellent problem-solving skills and a proactive attitude.
• Good communication and teamwork skills, with a willingness to collaborate and learn from others.
• Conceptual understanding of Data Science lifecycle
• Growth mindset: Ability to adapt and learn with changing situations.

UKG is proud to be an equal opportunity employer and is committed to promoting diversity and inclusion in the workplace, including the recruitment process.

Disability Accommodation:

For individuals with disabilities that need additional assistance at any point in the application and interview process, please email [email protected].