Eureka Labs
Eureka Labs is an innovative company focused on developing cutting-edge solutions in technology and software. With a commitment to excellence, it aims to provide powerful tools and services that enhance user experiences across various sectors. Their expertise lies in creating applications that drive efficiency and productivity for businesses and consumers alike.
Managed Teams • Nearshore Software Development • digital transformation • digital acceleration • nearshore technology solutions
Machine Learning Engineer
4 days ago
Eureka Labs
Eureka Labs is an innovative company focused on developing cutting-edge solutions in technology and software. With a commitment to excellence, it aims to provide powerful tools and services that enhance user experiences across various sectors. Their expertise lies in creating applications that drive efficiency and productivity for businesses and consumers alike.
Managed Teams • Nearshore Software Development • digital transformation • digital acceleration • nearshore technology solutions
📋 Description
• 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.
🎯 Requirements
• 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
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