DevOps / MLOps Engineer (Backend-Integrated)

Dev

Ops / MLOps Engineer (Cloud, AI Deployment, Security)

Duration 6 months

Remote (EST Time)

Overview:

We are building an AI-powered web platform and are looking for a Dev

Ops / MLOps Engineer to take ownership of infrastructure, deployment, and system reliability. This is a full-time role responsible for ensuring that backend services, AI systems, and real-time applications run securely, efficiently, and at scale.

In addition to Dev

Ops responsibilities, this role will actively contribute to backend development when needed, working closely with the engineering team across APIs, infrastructure, and system architecture.

Responsibilities:

• Design, deploy, and manage cloud infrastructure (AWS / GCP or similar)

• Deploy and maintain backend services (FastAPI) in production environments

• Contribute to backend development and system architecture when needed

• Deploy and manage AI/ML models and pipelines (MLOps workflows)

• Build and maintain CI/CD pipelines for automated testing and deployment

• Manage containerization (Docker) and orchestration (Kubernetes or similar)

• Handle GPU-based deployments and optimization for AI workloads

• Ensure system security, including:

• Authentication and access control

• API security and rate limiting

• Infrastructure hardening and monitoring

• Monitor system performance, uptime, logs, and alerts

• Implement backup, disaster recovery, and scaling strategies

• Optimize cloud infrastructure for performance and cost efficiency

• Support real-time systems (Web

Sockets, streaming, Live

Kit or similar)

• Work with databases in production environments (PostgreSQL, Supabase)

Requirements:

• Strong experience with cloud platforms (AWS, GCP, or Azure)

• Strong knowledge of Docker and container-based deployments

• Experience with CI/CD pipelines (Git

Hub Actions or similar)

• Solid backend development experience (Python, FastAPI or similar)

• Experience managing databases in production (PostgreSQL)

• Experience deploying and maintaining ML/AI models in production

• Familiarity with GPU infrastructure and optimization

• Understanding of security best practices for web and API systems

• Experience with Kubernetes or similar orchestration tools