ML Engineer (MLOps)

#lookfor #vacancy #remote #ml #machinelearning #python #mlops #docker #fastapi

We are looking for an experienced ML Engineer / ML Developer with MLOps skills to join remote outstaff projects for our partner companies.
This is a role for an ML developer who can build production-ready ML solutions: from data preparation and model development to deployment, monitoring and integration into real products.

Level: Middle+ / Senior
Format: Remote
Engagement: Full-time / long-term project-based cooperation
English: B2+ required
Employment: possible official employment or contract cooperation in RF / RB / EU depending on the project and candidate location

Must-have:
• 3+ years of commercial experience in ML with strong Python skills;
• clean, modular, maintainable production-level code;
• strong knowledge of classical ML and model evaluation;
• PyTorch / TensorFlow / JAX, scikit-learn, pandas, NumPy;
• data preprocessing and feature engineering;
• model training, validation and evaluation;
• experience building ML pipelines, deploying ML models or ML services to production;
• SQL, Git, Docker;
• FastAPI / Flask or similar tools for model inference;
• understanding of MLOps: reproducibility, versioning, experiment tracking, CI/CD, monitoring;
• experience with MLflow / DVC / Kubeflow / Airflow / W&B / Evidently AI or similar tools;
• understanding of model monitoring, data drift and production quality metrics;
• Spark / PySpark / Dask;
• English B2+.

Nice to have:
• NLP / CV / recommender systems / ranking / time series;
• Kubernetes;
• AWS SageMaker / GCP Vertex AI / Azure ML;
• Triton Inference Server / TensorFlow Serving / BentoML / KServe;
• Prometheus / Grafana / Evidently AI;
• GitLab CI / GitHub Actions / Jenkins;
• mentorship experience.

Responsibilities:
• develop and optimize ML models;
• prepare data and build ML pipelines;
• train, validate and evaluate models;
• package and deploy models to production;
• build APIs for model inference;
• set up basic MLOps processes;
• monitor model quality and production stability;
• collaborate with backend, data, DevOps and product teams.

Contacts:
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Please send your CV in English with your rate, availability, location, preferred cooperation format and examples of ML models or ML services deployed to production.