Machine Learning Engineer

Role Description As a Senior Data Science Engineer, you will play a critical role in the technical implementation of data science and machine learning solutions. You will collaborate closely with data scientists, data engineers, and data analysts to deliver high-quality, scalable ML models that address complex business challenges. Your focus will be on hands-on development, deployment, and maintenance of machine learning systems, ensuring robust standards and best practices are followed throughout the ML lifecycle. Responsibilities Initial Setup and Assessment • Collaborate with data scientists, engineers, and stakeholders to understand business requirements. • Review current deployment processes, infrastructure, and identify areas of improvement. • Set up and configure GCP Vertex AI environments, IAM roles, and supporting tools. • Define and document standards for model deployment, versioning, and monitoring. Development and Implementation • Build scalable ML training and prediction pipelines (batch and online) using Vertex AI. • Automate data preprocessing and feature engineering steps within pipelines. • Develop CI/CD pipelines with integrated testing, validation, and automated deployments. • Containerize models with Docker and deploy on GCP (Kubernetes Engine or Cloud Run). • Implement deployment strategies including rolling updates and rollback mechanisms. Testing, Optimization, and Monitoring • Conduct comprehensive testing of training and prediction pipelines, including load and stress testing. • Validate and optimize model performance, ensuring cost-efficiency and scalability. • Set up monitoring and alerting systems with Vertex AI Model Monitoring to track model health and detect issues in real time. Documentation, Knowledge Transfer, and Continuous Improvement • Document all processes, pipelines, and established best practices. • Conduct internal workshops and training sessions to enable team knowledge-sharing. • Review deliverables with stakeholders, gather feedback, and deliver final reports with recommendations. Qualifications Educational Background • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. Technical Skills • Programming: Proficiency in Python; familiarity with Java, Node.js, or C++. • ML Frameworks: Experience with Scikit-learn, TensorFlow, PyTorch, or similar. • Cloud Platforms: Experience designing and running ML workloads on cloud platforms, with a focus on Google Cloud Platform (GCP) and Vertex AI. • MLOps Tools: Proficiency in CI/CD, Docker, Kubernetes, and orchestration workflows. • Data Engineering: Skilled in data warehousing and processing tools, particularly BigQuery. • Version Control: Hands-on experience with Git and GitHub. • Experience Proven track record of deploying ML models in production (batch and online). • Experience building and maintaining ML pipelines. • Ability to propose and implement MLOps standards and best practices. • Experience working in Agile environments, with effective collaboration across teams. • Preferred Certifications (not required) • Google Cloud Professional Machine Learning Engineer • Google Cloud Professional Data Engineer • Google Cloud Professional Cloud Architect