Machine Learning Engineer

MLOps Engineer

Location: Remote / Hybrid

About Tymestack: At Tymestack, we're building a next-generation forecasting & optimization platform powered by AI. Our goal is to revolutionize how businesses predict and act on future trends, enabling better decision-making with precise, scalable, and real-time insights. We are seeking a skilled MLOps Engineer to join our team and help us build a robust, automated, and scalable machine learning infrastructure.

About the Role: As an MLOps Engineer, you will play a critical role in developing and enhancing our MLOps and CI/CD pipelines for our forecasting products. You will work closely with data scientists and engineers to automate and streamline model training, deployment, and monitoring using Google Cloud Platform (GCP) and Vertex AI. This role requires deep experience in creating scalable CI/CD pipelines, enabling data drift detection, automated retraining, and parallelizing hyperparameter tuning using Kubernetes.

Responsibilities:

• Design, implement, and maintain CI/CD/CT pipelines to automate every phase of the machine learning lifecycle from data ingestion, model training, testing, and deployment to production.
• Continuous Integration (CI): Build CI pipelines to automatically test and validate data, code, and model updates. Integrate with source control systems (e.g., Git) to trigger workflows on code changes.
• Continuous Delivery (CD): Automate model deployment to production environments using tools like Google Cloud Build and Vertex AI, ensuring that models are delivered continuously without downtime.
• Continuous Training (CT): Implement data drift detection mechanisms and establish automated model retraining workflows to ensure model accuracy over time. Regularly monitor model performance in production, retrain when needed, and ensure the latest data is incorporated into the model.
• Automated Testing: Implement automated testing frameworks to validate data integrity, model performance, and drift detection at every stage of the pipeline.
• Model Versioning: Establish automated versioning of models and datasets to ensure seamless rollback in case of failures.
• Monitoring and Alerts: Integrate monitoring tools to track pipeline performance, model accuracy, and system health. Implement alerting systems for data drift, failed deployments, and performance bottlenecks.
• Work on Google Cloud services, including Vertex AI, Kubernetes, and other GCP tools to build scalable, reliable infrastructure.
• Parallelize hyperparameter tuning and model training using Kubernetes to optimize computational resources.
• Develop robust infrastructure-as-code solutions using Terraform or similar tools to automate cloud resource provisioning and management.
• Collaborate with cross-functional teams, including data scientists and software engineers, to ensure seamless integration of machine learning models into production environments.
• Maintain the scalability, performance, and reliability of deployed models by implementing auto-scaling mechanisms in Kubernetes clusters.
• Continuously improve the pipelines by adopting new tools and techniques, optimizing workflows for faster deployments and better model performance.

Requirements:

• Proven experience (3+ years) in MLOps, DevOps, or related fields, with a focus on automating machine learning workflows.
• Expertise in Google Cloud Platform (GCP), including Vertex AI, Kubernetes, and other key services.
• Hands-on experience designing and managing CI/CD pipelines for machine learning, including testing, versioning, and monitoring.
• Proficiency in scripting and automation (Python, Bash) and experience with infrastructure-as-code tools like Terraform.
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of data drift detection, automated model retraining, auto-scaling, and hyperparameter tuning in distributed machine learning systems.
• Familiarity with data pipeline tools and orchestration frameworks (e.g., Dataflow).
• Strong problem-solving skills with a proactive approach to troubleshooting and resolving issues.
• Excellent communication skills and a collaborative mindset.

Nice-to-Have:

• Experience with other cloud platforms (AWS, Azure).
• Familiarity with ML frameworks such as XGBoost, BigQuery ML, TensorFlow or PyTorch.
• Prior experience in working with time-series data or forecasting models.

What We Offer:

• Competitive salary.
• Flexible remote work opportunities with a hybrid option.
• Opportunities for professional development and growth within a fast-paced, innovative company.
• Work with a passionate team committed to revolutionizing forecasting with cutting-edge technology.

How to Apply:

If you're interested in joining our innovative team, we have two small tasks that will help us assess your skills and suitability for the role: https://drive.google.com/file/d/1buktQOL7IOpuLws0Q-ow5_TH3oDRbiV1/view?usp=sharing

Please complete both the tasks and submit the deliverables as described along with your resume to [email protected].