Machine Learning Engineer (25MLE02AD)

Eureka Labs

Argentina · Full-time · Not Applicable

Overview

Location: Full remote.

Schedule: Full time, European time zone availability.

Job Purpose

We are seeking a Senior Machine Learning Engineer with a strong focus on MLOps. In this role, you will design and maintain automated ML systems—from training pipelines to production inference services. You will collaborate closely with data scientists and software engineers to deliver end-to-end solutions and ensure our ML infrastructure is robust, scalable, and efficient. Embracing a culture of shared ownership, you will contribute to a high-performing and resilient platform.

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 integrate ML solutions seamlessly.
Ensure best practices in model versioning, monitoring, and deployment for maintainable ML systems.
Participate in on-call rotations to ensure high reliability and availability of ML services

Experience & Qualifications

Experience with containerization and microservices, including Docker or similar technologies.
Expertise 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

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.

Key Skills

Ranked by relevance

mlops machine learning kubeflow spark infrastructure as code containerization microservices kubernetes tensorflow terraform big data fastapi pytorch python docker apache pandas mlflow cloud numpy cicd aws s3

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