Sr. Machine Learning Engineer (MLOps) Advanced English

• Location: Full Remote. Must be available during European time zone (Italy)
• Seniority Level: Senior
• Language: Advanced English is required for the position.

We are seeking a Machine Learning Engineer with a strong focus on MLOps to support the operationalization and scaling of machine learning solutions. This role involves automating ML pipelines, deploying models into production, and ensuring performance and reliability over time.

The successful candidate will collaborate closely with data scientists and software engineers to build end-to-end systems that are robust, maintainable, and efficient.

  Key Responsibilities

• Design, build, and maintain automated ML pipelines for training and deployment.

• Develop and manage production-grade inference services (e.g., APIs serving ML models).

• Implement model versioning, monitoring, and testing processes.

• Collaborate with data scientists and backend engineers to integrate ML models into production systems.

• Ensure system reliability and performance through best practices and observability tools.

• Participate in on-call rotations to support production ML environments.

  Requirements

• Proven experience designing and automating end-to-end ML pipelines.

• Strong programming skills in Python and familiarity with libraries such as NumPy, pandas, TensorFlow or PyTorch.

• Hands-on experience with containerization (e.g., Docker) and microservices architecture.

• Experience with MLOps tools like Kubeflow Pipelines, MLflow, or similar.

• Solid understanding of CI/CD practices and model versioning strategies.

• Experience deploying models in cloud environments (preferably AWS: EC2, S3, EKS).

• Ability to build and serve ML models via APIs (experience with FastAPI is a plus).

• Familiarity with software engineering best practices (code reviews, testing, version control).

• Advanced English communication skills (spoken and written).

  Nice to Have

• Experience with Infrastructure as Code tools (e.g., Terraform).

• Familiarity with Kubernetes (pods, resource management, orchestration).

• Exposure to big data frameworks (e.g., Apache Spark) for scalable data processing.

  Benefits

• 3 weeks of paid vacation per year

• Paid sick leave

• Flexible use of Argentine national holidays

• Half day off on your birthday

• Annual training budget

• Gifts throughout the year

• Autonomy and ownership in your day-to-day work

• Contractor position

• Flexible, collaborative, and supportive team culture

• Opportunities to work on impactful and scalable ML systems