Middle ML Engineer

Описание вакансии

#lookfor #outsource #outstaff #remote #MLEngineer #MachineLearning #MLOps #Python #PyTorch #TensorFlow #FastAPI #Docker #Kubernetes

We are looking for a Middle ML Engineer to join our AI/ML team on a full-time remote basis.

The specialist will design, build, and deploy machine learning models into production, develop MLOps pipelines, and manage the end-to-end ML lifecycle from training to monitoring and API integration.

Key responsibilities:

• 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 (FastAPI, Flask) 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 (MLflow, Weights & Biases).

• 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.

Requirements:

• 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 (FastAPI, Flask) for model serving.

• Experience with SQL databases and data querying for feature extraction.

• Familiarity with cloud ML platforms (AWS SageMaker, Azure ML, Vertex AI).

• 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 (Celery, RabbitMQ, Kafka).

• 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 (LLMs, LangChain, Hugging Face).

• Familiarity with columnar databases (ClickHouse, BigQuery, Redshift).

• Experience with feature stores (Feast, Tecton) and model monitoring tools.

• Knowledge of C++ or Rust for high-performance model serving.

• Understanding of Bayesian methods, Apache Spark, or serverless deployment.

• Contributions to open-source ML projects or research publications.

Location: Remote, worldwide

Restrictions: Candidates from Egypt, India, Pakistan, and Afghanistan are not considered

English: B2+

Format: Full-time, outsource, outstaff

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