Job Title: Senior Machine Learning Engineer (MLOps) - 12-Month Contract, Fully Remote in the EU
Compensation: $5,000 USD + VAT per month (Gross) + 29 days holidays
Job Overview:
We are seeking a Senior Machine Learning Engineer with a strong focus on MLOps to design and maintain automated ML systems. In this role, you will develop and optimize training pipelines, manage production inference services, and ensure the robustness and scalability of our ML infrastructure. You will work closely with data scientists and software engineers to deliver end-to-end solutions while fostering a culture of shared ownership and high performance.
Key Responsibilities:
* Design, develop, and maintain MLOps systems to automate ML workflows.
* Create and optimize training pipelines for machine learning models.
* Implement and manage inference services for production environments.
* Collaborate with data scientists and software engineers to seamlessly integrate ML solutions.
* Ensure best practices in model versioning, monitoring, and deployment for maintainable ML systems.
* Participate in on-call rotations to maintain high reliability and availability of ML services.
Experience & Qualifications:
* Expertise in containerization and microservices, including Docker or similar technologies.
* Proven experience in automating end-to-end ML pipelines, integrating CI/CD workflows, and monitoring model performance.
* Proficiency in data versioning, experiment tracking, and model serving technologies (e.g., TensorFlow Serving, TorchServe).
* Strong Python skills and familiarity with Data Science frameworks (e.g., NumPy, pandas, PyTorch, TensorFlow).
* Experience with cloud platforms, particularly AWS (e.g., EC2, S3, EKS).
* Hands-on experience with MLOps tools such as Kubeflow Pipelines, MLflow, and FastAPI.
* Knowledge of big data frameworks like Apache Spark, including writing and optimizing Spark jobs.
* Strong software engineering principles, including version control, code reviews, and testing best practices.
* Proven ability to design, build, and optimize scalable ML training workflows and low-latency inference endpoints.
* Skilled in setting up and customizing Kubeflow Pipelines for ML training and deployment.
* Self-sufficient problem-solver with strong prioritization skills and ability to work collaboratively.
* Advanced English level (both written and verbal communication).
Nice to Have:
* FastAPI: Familiarity with lightweight REST API development.
* Terraform: Understanding of Infrastructure as Code principles to automate resource provisioning.
* Kubernetes: Foundational skills in container orchestration, Pod deployment, and resource management.