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Remote role, mid-level experience band, and broad cloud+ML skill requirements increase applicant competition.
Role requires specialized cloud and MLOps/LLM skills that are moderately transferable across industries.
Multiple mandatory technical filters (containers, Terraform, cloud, MLOps, LLM experience) create strict screening criteria.
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Design, deploy, and maintain containerized AI/ML applications using Docker and Kubernetes for high availability and scalability.
Build and manage CI/CD pipelines with Terraform to automate ML/LLM model deployment, versioning, monitoring, and infrastructure provisioning.
Develop infrastructure and microservices to support AI agent orchestration, multi-agent collaboration, and observability ensuring reliability and performance of production-grade AI systems.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
2+ years of experience in cloud infrastructure, DevOps, or platform engineering.
Strong hands-on experience with containerization technologies such as Docker.
Experience with cloud platforms (AWS, Azure, or GCP) and building CI/CD pipelines using Terraform for infrastructure automation.
Experienced in Kubernetes cluster management and orchestration for AI/ML workloads.
Knowledgeable in MLOps principles including model lifecycle management, monitoring, and deployment of large language models (LLMs).
Practiced in scripting for automation and familiar with monitoring and observability tools (e.g., Prometheus, Grafana, ELK) and collaborative development workflows (Git).