Sr. Machine Learning Engineer (MLOps) Advanced English

Consultora Gestal; United States; Remote; USD 100,000 - 160,000; Full time

Job summary

A leading company seeks a Senior Machine Learning Engineer (MLOps) to operationalize and scale ML solutions in a full remote capacity. Ideal candidates will automate ML pipelines, collaborate with data teams, and ensure model performance and reliability in production environments. The role offers a flexible and supportive team culture with impactful work on scalable ML systems.

Benefits

3 weeks of paid vacation per year

Paid sick leave

Flexible use of Argentine national holidays

Half day off on your birthday

Gifts throughout the year

Autonomy in work

Collaborative team culture

Qualifications

• Proven experience in designing and automating ML pipelines.
• Strong programming skills in Python with ML libraries.
• Hands-on with containerization and MLOps tools.

Responsibilities

• Design and maintain automated ML pipelines.
• Develop production-grade inference services and APIs.
• Implement model versioning and monitoring processes.

Skills

Python

MLOps

CI/CD practices

Containerization

Machine Learning frameworks

APIs

Advanced English

Tools

Docker

Kubeflow

MLflow

AWS

Terraform

Kubernetes

Apache Spark

Job description

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.

3 weeks of paid vacation per year

Paid sick leave

Flexible use of Argentine national holidays

Half day off on your birthday

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