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Mid-level metro MLOps role with niche LLM-ops skills yields moderate competition.
Core MLOps skills are transferable but LLM and on-prem industrial experience increase domain specificity.
Multiple mandatory technical requirements and a 3+ year minimum create strict shortlisting.
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Own the end-to-end operational lifecycle of ML and LLM systems, ensuring reliable production-grade deployment and maintenance.
Build and maintain automated CI/CD pipelines for model training, deployment, and serving, including quality monitoring and drift detection.
Operate LLM workflows involving prompt versioning, evaluation, guardrails, inference optimization, and ensure system reliability and security.
Bachelor's degree in engineering or higher.
3+ years experience in ML Ops, ML platform, or ML infrastructure engineering.
Strong hands-on experience with MLflow, CI/CD automation for ML, Docker, Kubernetes/K3s, and cloud platforms (Azure, GCP, AWS) plus Databricks.
Experience operating LLM applications including inference serving, evaluation, guardrails, and model quality monitoring; strong Python software engineering fundamentals.
Experienced in deploying and maintaining ML and open source LLM models in production and on-prem/edge environments, especially for industrial or resource-constrained settings.
Capable of independently managing full ML Ops lifecycles from experimentation through production at scale with ownership mindset.
Familiarity with inference serving frameworks such as vLLM, Triton, or Ollama and real-time or time series ML systems for sensor/IoT data is a strong advantage.