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Niche MLOps/LLM skills but metro location and mid-level experience increase competition.
Core MLOps skills transfer across industries, but on-prem/edge industrial experience increases sensitivity.
Explicit 3+ years and many mandatory MLOps, cloud, Databricks, and Kubernetes requirements raise strictness.
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Own the end-to-end operational lifecycle of ML and LLM systems, advancing models from experimentation to production-grade, scalable environments.
Build and maintain automated CI/CD pipelines for model training, deployment, and serving with a focus on reliability, security, and reproducibility.
Operate LLM workflows including prompt versioning, evaluation, guardrails, inference optimization, and perform quality monitoring and drift detection for models in production.
Bachelor's degree in engineering or higher.
Minimum 3+ years of experience in ML Ops, ML platform, or ML infrastructure engineering.
Strong experience deploying, serving, and maintaining ML models in production, including hands-on with MLflow and CI/CD automation.
Proficiency with Docker, Kubernetes/K3s, major cloud platforms (Azure, GCP, AWS), Databricks, and experience operating LLM applications (inference serving, evaluation, guardrails).
Experienced in building and operating ML and GenAI systems reliably at scale, including in on-premise and edge environments common in industrial settings.
Has demonstrated ownership of ML infrastructure projects taking concepts to production with strong software engineering skills, especially in Python.
Familiar with specialized inference serving frameworks and deploying open source LLMs to constrained or air-gapped environments preferred.