Machine Learning Engineer

As an integral part of our team, you will collaborate closely with Data Scientists, Data Engineers, Product Managers, and stakeholders to design, implement, deploy, and ensure seamless operation of machine learning models and systems. You will be responsible for developing advanced ML models, as well as automating, scaling, and maintaining end-to-end ML pipelines using industry-standard tools and methodologies.

This position involves the full spectrum of machine learning development, from initial model creation and training to deployment, monitoring, and continuous improvement. You will leverage your experience with ML frameworks, MLOps tools, and cloud infrastructure to deliver robust, scalable, and high-performing machine learning solutions.

We seek candidates with deep technical proficiency in machine learning development and statistical modelling, strong problem-solving capabilities, commitment to quality assurance, and passion for continuous improvement of software engineering and operational practices in the machine learning domain.

Responsibilities:

• Design, develop, and deploy machine learning models using frameworks like TensorFlow, PyTorch, Scikit-learn, Prophet, CatBoost, and XGBoost, etc.
• Build, automate, and manage end-to-end ML pipelines using MLOps frameworks including Kubeflow, MLflow, Vertex AI, and SageMaker.
• Implement and maintain CI/CD processes with tools such as Google Cloud Build, GitHub Actions, Jenkins, or Azure DevOps.
• Deploy, scale, and manage ML models on cloud platforms (GCP and AWS) using containerisation and orchestration tools such as Docker and Kubernetes.
• Write efficient, maintainable, and production-grade Python code following best practices in object-oriented programming, code refactoring, and software design patterns.
• Develop and maintain robust unit tests using pytest or equivalent testing frameworks, ensuring high-quality software development practices.
• Establish comprehensive monitoring, logging, and observability systems using Vertex AI Model Monitoring, Grafana, and similar tools.
• Manage the ML model lifecycle including versioning, packaging, serving, deployment, monitoring, and iterative improvements.
• Maintain best practices in version control (Git), including branching strategies and release management. Conduct and manage code reviews, including comprehensive pull request (PR) reviews, ensuring adherence to coding standards and best practices.
• Collaborate effectively across teams to diagnose issues, implement improvements, and proactively mitigate operational risks.

Skills and Experience:

• Master's or PhD in Computer Science, Engineering, Data Science, or related technical fields, or equivalent practical experience.
• 3-5 years of professional experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
• Strong expertise with ML frameworks (TensorFlow, PyTorch, Scikit-learn, Prophet, CatBoost, XGBoost).
• Proven experience with MLOps tools such as Kubeflow, MLflow, Vertex AI, and SageMaker.
• Extensive hands-on experience with CI/CD tools.
• Solid experience deploying ML solutions on GCP and AWS using Docker and Kubernetes.
• Proficient Python programming skills with deep understanding of OOP principles, code refactoring techniques, and automation scripting (Bash/Shell).
• Expertise in unit testing frameworks like pytest, emphasizing Test-Driven Development (TDD).
• Competent in monitoring and observability practices.
• Experienced in managing ML model lifecycle and version control using Git.
• Familiarity with agile software development practices and methodologies.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent communication skills to interact effectively with technical and non-technical stakeholders.

Desirable:

• Certifications in cloud platforms (GCP, AWS).
• Familiarity with data engineering tools and big data technologies.
• Understanding of security and compliance standards, including GDPR and data privacy.

This role offers an exciting opportunity to contribute directly to cutting-edge machine learning solutions, ensuring reliability, scalability, and maintainability, and significantly impacting operational efficiency and innovation.

What we believe:

We’re proud to embrace the same values that have shaped UST since the beginning. Since day one, we’ve been building enduring relationships and a culture of integrity. And today, it's those same values that are inspiring us to encourage innovation from everyone to champion diversity and inclusion, and to place people at the center of everything we do.

Humility:

We will listen, learn, be empathetic and help selflessly in our interactions with everyone.

Humanity:

Through business, we will better the lives of those less fortunate than ourselves.

Integrity:

We honor our commitments and act with responsibility in all our relationships.

Equal Employment Opportunity Statement

UST is an Equal Opportunity Employer.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.