Middle ML Engineer (Python, FastAPI)

#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
Contact: ••••••••