ML Engineer

As an ML Engineer, you will build, productionize, and scale machine learning systems, bridging the gap between model development and reliable, real-world deployment. You will focus on the engineering side of the ML lifecycle – from data pipelines and training infrastructure to deployment, monitoring, and performance optimization. 

You will collaborate closely with data scientists, software engineers, and product stakeholders to turn experimental models into robust, scalable services that run efficiently in production. 

Key Responsabilities

• Design, build, and maintain scalable infrastructure for training, deploying, and serving machine learning models. 
• Take models developed by data scientists from prototype to production-grade services. 
• Build and automate ML pipelines for data processing, training, validation, and deployment (MLOps). 
• Optimize model inference for latency, throughput, and cost efficiency in production environments. 
• Implement monitoring and alerting for model performance, data drift, and system health. 
• Collaborate with data scientists to refine models for scalability, maintainability, and production readiness. 
• Design and maintain CI/CD pipelines for machine learning workflows. 
• Ensure ML systems meet security, reliability, and compliance standards. 
• Document system architecture, pipelines, and operational processes. 
• Stay current with new tools and best practices in ML engineering and MLOps, proposing their adoption when relevant. 

Job Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 
• 5+ years of experience in an ML Engineer, Software Engineer, or similar role with a strong ML component. 
• Strong software engineering fundamentals, including data structures, algorithms, and system design. 
• Proven experience deploying and maintaining machine learning models in production. 
• Solid understanding of machine learning concepts and the model development lifecycle. 
• Strong problem-solving skills and comfort working across the engineering/data science boundary. 
• Fluency in English (written and spoken); additional language is a plus. 
• Ability to work independently and as part of a distributed/remote team.

Main Tech Skills

• Programming Languages: Python (required); Java, Go, or Scala a plus. 
• ML Frameworks: TensorFlow, PyTorch, scikit-learn. 
• MLOps & Orchestration: MLflow, Kubeflow, Airflow, DVC. 
• Model Serving: TensorFlow Serving, TorchServe, FastAPI, gRPC. 
• Containerization & Orchestration: Docker, Kubernetes. 
• Cloud Platforms: AWS (SageMaker), Azure (Azure ML), or GCP (Vertex AI). 
• CI/CD: GitHub Actions, GitLab CI, Jenkins. 
• Monitoring & Observability: Prometheus, Grafana, Evidently AI, or similar tools. 
• Data Engineering Basics: Spark, SQL, data pipeline concepts. 
• Infrastructure as Code: Terraform or CloudFormation (a plus).