🛠 Tech Stack

💼 About This Role

You'll bridge data science and production engineering by building and optimizing scalable ML infrastructure. You'll automate complex workflows and ensure reliability of enterprise-level models. This role offers 100% remote work with top US companies and competitive USD pay.

🎯 What You'll Do

• Design and maintain robust ML deployment pipelines
• Automate model training, deployment, and monitoring workflows
• Collaborate with data scientists to integrate models into production
• Optimize cloud-based infrastructure for scalability and reliability

📋 Requirements

• 3+ years as MLOps or Machine Learning Engineer in production environments
• Advanced proficiency in Python
• Experience with Docker and Kubernetes for containerization/orchestration
• Expertise in cloud platforms like AWS, Azure, or GCP

✨ Nice to Have

• Experience with Kubeflow, Airflow, or MLFlow
• Familiarity with LLMOps or GenAI pipelines
• Background in enterprise-scale infrastructure or regulated environments

🎁 Benefits & Perks

• 🏠 100% Remote Work
• 💰 Competitive USD Pay
• 🏖️ Paid Time Off
• ⏰ Work with Autonomy
• 🏢 Work with Top American Companies