Middle ML Engineer

We’re looking for a Middle ML Engineer to join a long-term outstaff project with an international R&D team.
You’ll work on developing, deploying, and monitoring ML solutions in the cloud from data and feature engineering to production-grade model deployment.
 

Requirements

• 3+ years of experience deploying ML models (classical or LLM-based) in cloud environments (AWS/GCP/Azure) using containers and Kubernetes
• Practical experience with MLOps tools: MLflow, Kubeflow, SageMaker, Vertex AI, Feast, or similar
• Hands-on experience building and maintaining data and feature pipelines
• Understanding of real-time model monitoring (drift, latency, performance)
• Solid software engineering background: version control, CI/CD, testing, cost optimization
• English: Upper-Intermediate or higher

Nice to have:

• Experience with PyTorch or TensorFlow
• Familiarity with Spark or Ray

• Knowledge of feature stores and data governance

   

Responsibilities

• Design, implement, and maintain end-to-end ML pipelines for both classical and LLM-based models
• Deploy and manage ML models in AWS/GCP/Azure using Docker and Kubernetes
• Apply MLOps best practices for reproducibility, monitoring, and automation
• Build and maintain robust data and feature pipelines for ML
• Implement real-time monitoring for model drift, latency, and performance
• Collaborate with Data Scientists, DevOps, and Product teams to integrate ML solutions into production
• Contribute to engineering best practices, CI/CD, testing, and cost optimization