Job description
We’re looking for a passionate MLOps Engineer to join our innovative Data Science team. 🚀
You’ll be the crucial link between our machine learning models and our production environment, responsible for building the infrastructure that allows our data scientists to create and deploy cutting-edge solutions at scale.
In this role, you won’t just be deploying models; you’ll be building and automating the entire ML lifecycle. If you love solving complex problems and want to productionize state-of-the-art AI, this is the perfect opportunity for you.
Key Responsibilities
- Design and Build ML Infrastructure: Create, manage, and scale the infrastructure required for training and deploying our machine learning models.
- Automate ML Pipelines: Develop and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for the full ML lifecycle.
- Deploy & Serve Models: Implement strategies for deploying models as scalable, reliable services using technologies like containerization (Docker, Kubernetes) and serverless functions.
- Monitor Model Performance: Establish and manage comprehensive monitoring solutions to track model accuracy, data drift, and system health to ensure our models perform as expected in production.
- Collaborate Cross-Functionally: Work closely with data scientists to understand model requirements and with software engineers to integrate ML models into our core products.
- Champion Best Practices: Advocate for and implement MLOps best practices in versioning (data, code, models), testing, and security across the team.
Required (Must-Have)
- Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- Proven experience in a DevOps, Software Engineering, or MLOps role.
- Strong programming skills, particularly in Python.
- Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure) and its ML services (e.g., SageMaker, Vertex AI, Azure ML).
- Solid experience with containerization (Docker) and orchestration (Kubernetes).
- Experience building and managing CI/CD pipelines using tools like GitLab CI, GitHub Actions, or Jenkins.
- A solid understanding of the end-to-end machine learning lifecycle.
Preferred
Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
Familiarity with MLOps frameworks like MLflow, Kubeflow, or Vertex AI Pipelines.
Experience with data processing frameworks such as Apache Spark or data workflow tools like Airflow.
Knowledge of model monitoring tools like Prometheus, Grafana, or Evidently AI.
Competitive base salary with additional performance incentives.
Coverage under the company’s collective health insurance plan.
Learning and development opportunities (e.g. onboarding, on-the-job training).
Annual training budget.
Hybrid work model & extra personal/flex days and paid volunteer days a year for your favorite cause.
Company sponsored team-bonding events.
Weekly health & wellness activities (e.g. basketball, football, yoga, running), gym discounts, healthy breakfast, snacks and beverages.
Entrepreneurial culture and amazing coworkers!
