Cloud-Native ML Engineer(Freelance / Remote)

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

Cloud-Native ML Engineer

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