AI Engineer / Machine Learning Engineer – MLOps
# AI Engineer / Machine Learning Engineer – MLOps **KATBOTZ** · United States · `Remote` · `Contractor` 💼 **Уровень роли:** `Mid-level` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineer]` `[MLOps-Engineer]` `[AI-Engineer]` `[DevOps-Engineer]` `[Data-Engineer]` `[MLOps-Engineer-Jobs]` `[AI-ML-Engineer]` `[AI-ML-Ops-Engineer]` `[AI-ML-Engineer-Jobs]` `[AI-ML-Software-Engineer]` --- ### About the Role This is a remote position. AI Engineer / Machine Learning Engineer – MLOps We are looking for an AI Engineer with strong experience in Machine Learning Operations (MLOps) to design, deploy, monitor, and maintain machine learning and AI models in production environments. The candidate will be responsible for building scalable ML pipelines, automating model deployment, managing model lifecycle, and ensuring reliability, performance, and governance of AI systems. Key Responsibilities - Build and maintain ML pipelines for training, testing, and deployment - Deploy machine learning and AI models into production environments - Manage model lifecycle (training, deployment, monitoring, retraining) - Automate workflows using CI/CD for ML models - Monitor model performance, drift, and data quality - Work with data scientists and AI developers to productionize models - Manage model versioning, data versioning, and experiment tracking - Deploy models on cloud platforms (AWS, Azure, GCP) - Containerize applications using Docker and Kubernetes - Implement monitoring and logging for ML systems - Ensure scalability, security, and reliability of AI systems Requirements Required Skills - Python - Machine Learning - MLOps tools and frameworks - Docker - Kubernetes - CI/CD (GitHub Actions, Jenkins, GitLab CI) - MLflow / Kubeflow / Airflow - Data pipelines - APIs (FastAPI / Flask) - Cloud platforms (AWS / Azure / GCP) - SQL / NoSQL databases - Model monitoring and logging MLOps Tools (Important) Candidate should have experience in some of these: - MLflow - Kubeflow - Airflow - DVC - Weights & Biases - SageMaker - Azure ML - Vertex AI - Docker - Kubernetes - Terraform Experience Required - 3–7 years in Machine Learning / AI / Data Engineering - 2+ years in MLOps / Model Deployment / ML Pipelines - Experience deploying models to production is mandatory Education Benefits - Competitive compensation package - Opportunities for professional development and career advancement. - Flexible working conditions, with remote options available. - Dynamic and supportive work environment. Originally posted on Himalayas
- Machine-Learning-Engineer
- MLOps-Engineer
- AI-Engineer
- DevOps-Engineer
- Data-Engineer
- MLOps-Engineer-Jobs
- AI-ML-Engineer
- AI-ML-Ops-Engineer
- AI-ML-Engineer-Jobs
- AI-ML-Software-Engineer
Наблюдалась 2026-10-08, впервые 2026-10-08, источник — Himalayas (JSON API).