ml engineer

No description

• Develop, train, and optimize ML models for production use cases;
• Design and implement MLOps pipelines for model versioning, training, validation, and deployment;
• Deploy ML models using Docker, Kubernetes, and cloud services;
• Build high-performance REST APIs for model serving;
• Implement automated data preprocessing, feature engineering, and transformation pipelines;
• Monitor model performance, data drift, and prediction quality in production;
• Optimize inference latency, throughput, and resource consumption;
• Integrate ML services with backend systems and microservices architecture;
• Orchestrate ML workflows using Airflow, Prefect, or similar tools;
• Maintain experiment tracking and model registries;
• Collaborate with data scientists to productionize research prototypes;
• Implement A/B testing frameworks for model comparison and rollout;
• Ensure reproducibility of ML experiments and maintain documentation;
• Troubleshoot production ML issues and perform root cause analysis.

• 3+ Years of commercial experience in Machine Learning Engineering or related roles;
• Strong Python proficiency for ML development and system integration;
• Hands-on experience with ML frameworks: PyTorch, TensorFlow, or Scikit-learn;
• Practical knowledge of MLOps tools: MLflow, Airflow, Prefect, or Kubeflow;
• Experience deploying ML models to production using Docker and Kubernetes;
• Solid understanding of REST API development for model serving;
• Experience with SQL databases and data querying for feature extraction;
• Familiarity with cloud ML platforms;
• Understanding of CI/CD principles for ML pipelines and automated testing;
• Experience with Git and collaborative development workflows;
• Knowledge of model optimization: quantization, pruning, ONNX conversion;
• Understanding of distributed training and GPU computing basics;
• Familiarity with message brokers;
• Strong problem-solving skills and ability to bridge research and production;
• English: B2 or higher (written and spoken);
• Nice to have: Experience with NLP, Computer Vision, RAG, or Generative AI, familiarity with columnar databases, experience with feature stores and model monitoring tools, knowledge of C++ or Rust, understanding of Bayesian methods, Apache Spark, or serverless deployment, contributions to open-source ML projects or research publications.

• No conditions specified