Eureka Labs

Machine Learning Engineer (25MLE02AD)

Eureka Labs Córdoba, Córdoba, Argentina
Eureka Labs

Machine Learning Engineer (25MLE02AD)

Eureka Labs Córdoba, Córdoba, Argentina
Hace 1 semana 33 solicitudes

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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.


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.

  • Nivel de antigüedad

    No corresponde

  • Tipo de empleo

    Jornada completa

  • Función laboral

    Ingeniería y Tecnología de la información

  • Sectores

    Servicios y tecnologías de la información

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