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Title: Machine Learning Engineer
Location: Remote
Type: Contract
Role Description
As a Senior 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
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Equal Employment Opportunity Statement
Gravity IT Resources is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected characteristic. All employment decisions are based on qualifications, merit, and business needs.