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Machine Learning Engineer (25MLE02AD) at Eureka Labs. Location Information: Remote. . 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. . . .