Cloud-Native ML Engineer (Freelance | WFH)

About BeGig

BeGig is the leading tech freelancing marketplace. We empower innovative, early-stage, non-tech founders to bring their visions to life by connecting them with top-tier freelance talent. By joining BeGig, you’re not just taking on one role—you’re signing up for a platform that will continuously match you with high-impact opportunities tailored to your expertise.

Your Opportunity

Join our network as a Cloud-Native ML Engineer and build, deploy, and scale machine learning solutions that leverage the full power of cloud-native technologies. You’ll work closely with startups to enable rapid experimentation, seamless deployment, and robust scaling of ML pipelines and models across public cloud platforms.

This is a fully remote position, available on an hourly or project-based basis.

Role Overview

As a Cloud-Native ML Engineer, you will:

• Build ML Pipelines: Design and implement scalable ML pipelines using cloud-native tools and managed services.
• Model Deployment: Deploy and monitor machine learning models in production using Kubernetes, Docker, or serverless architectures.
• Orchestrate Workflows: Use workflow orchestration tools (e.g., Kubeflow, Airflow, Vertex AI Pipelines, Sagemaker Pipelines) to automate ML workflows.
• Optimize for Cloud: Tune model inference, resource allocation, and cost efficiency across AWS, GCP, or Azure.
• Integrate CI/CD: Develop and maintain CI/CD pipelines for continuous delivery of ML features and updates.
• Monitor & Scale: Implement monitoring, logging, and auto-scaling strategies to ensure robust, production-grade ML services.

Technical Requirements & Skills

• Experience: Minimum 2+ years in machine learning engineering or DevOps with hands-on cloud experience.
• Cloud Platforms: Proficiency in AWS, GCP, or Azure for deploying and managing ML workloads.
• Containerization: Experience with Docker, Kubernetes, or serverless frameworks for ML deployment.
• Workflow Tools: Hands-on with ML workflow orchestration tools such as Kubeflow, Airflow, Vertex AI, or Sagemaker.
• Programming: Strong Python skills; familiarity with ML libraries (scikit-learn, TensorFlow, PyTorch).
• Automation & Monitoring: Experience with CI/CD tools, monitoring (Prometheus, CloudWatch), and logging solutions.

What We’re Looking For

• An engineer passionate about making ML scalable, reliable, and cloud-optimized.
• A freelancer who can turn ML prototypes into robust, production-ready services in modern cloud environments.
• A systems thinker who proactively identifies bottlenecks, optimizes workflows, and drives automation.

Why Join Us?

• Immediate Impact: Enable startups to move faster and smarter with cloud-powered ML solutions.
• Remote & Flexible: Work from anywhere, on an hourly or project basis—tailor your engagements to your schedule.
• Future Opportunities: Get matched with roles in ML infrastructure, MLOps, and cloud-native data science.

Growth & Recognition: Join a trusted network where your skills in cloud ML engineering are highly valued.