Back
Metro location and mid-level experience increase competition despite niche MLOps specialization.
Role requires specialized LLM/MLOps and regulated-industry governance, limiting cross-industry transferability.
Explicit 5+ years, mandatory MLOps/LLMOps and cloud platform experience make filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own CI/CD pipelines and production deployment for ML and agentic AI systems on cloud-native platforms (AWS, Azure, or equivalent).
Build and maintain observability, monitoring, and security governance for LLM and agentic AI workloads in regulated environments.
Implement infrastructure-as-code and optimize cost/performance for AI/ML platforms, ensuring reliable operational scale from prototype to production.
5+ years experience in cloud/DevOps/MLOps engineering with AWS, Azure, or GCP.
Hands-on experience with production deployment of ML/GenAI systems including CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code tools (Terraform).
Proficiency with MLOps tools (e.g., MLflow, SageMaker Pipelines, Azure ML Pipelines) and cloud-native AI platforms (e.g., AWS Bedrock, SageMaker, Azure AI Foundry).
Strong scripting skills in Python and Bash plus security/governance experience in regulated environments (RBAC, secrets management, audit).
Deep operational knowledge of LLM and agentic AI systems including observability, cost monitoring, regression and hallucination testing.
Experience working in regulated sectors such as pharma, life sciences, or financial services is a strong plus.
Ability to collaborate cross-functionally with data engineers, AI engineers, and business stakeholders to scale AI systems securely and reliably into production.