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Metro mid-level MLOps role with common cloud/DevOps skills but niche LLM and vector DB requirements.
Requires specialized MLOps, LLM, and cloud production experience, limiting cross-industry transferability.
Explicit 6–10 years requirement plus mandatory cloud, Terraform, containerization, and ML-serving expertise.
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Design, deploy, and manage scalable AI/ML infrastructure supporting LLM agent systems, machine learning models, and data pipelines in production environments.
Build and maintain cloud-based infrastructure and services across AWS, GCP, or Azure, including containerized applications, cloud databases, messaging platforms, and ETL/ELT pipelines.
Develop and operate CI/CD pipelines, implement infrastructure as code using Terraform, and ensure monitoring, performance optimization, security, and governance of AI/ML platforms.
Bachelor's degree in Computer Science, Information Technology, Engineering, or related field.
6–10 years of experience in MLOps, DevOps, Cloud Engineering, or Infrastructure Engineering.
Proven hands-on experience deploying and managing production ML/AI systems on major cloud platforms (AWS, GCP, or Azure).
Strong Python programming skills and experience with Infrastructure as Code tools (Terraform preferred).
Deep expertise in at least one major cloud platform with experience in microservices, cloud databases, messaging infrastructure, and ML model serving.
Skilled in containerization technologies and building telemetry and monitoring platforms for ML workloads.
Experienced in building automated pipelines and advocating MLOps best practices including model lifecycle management and AI platform operations.