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High due to metro location, mid-level (4+ years), broad MLOps skillset, and popular AI engineer title.
Medium because core MLOps and cloud skills transfer across industries but require specialized ML engineering experience.
High because of explicit 4+ years requirement plus extensive mandatory MLOps, CI/CD, cloud, and tooling skills.
Build and maintain end-to-end ML pipelines including data ingestion, training, evaluation, packaging, versioning, and deployment.
Develop and manage CI/CD pipelines using Jenkins for multiple environments with multi-stage gates.
Deploy and optimize model-serving APIs on AKS with FastAPI and vLLM; implement observability and DevSecOps practices.
4+ years of experience in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
Strong experience with Jenkins CI/CD, Azure DevOps, GitOps, and Databricks ML pipelines including Delta Lake and MLflow.
Proficiency in Python (FastAPI, Pydantic, async), Bash, and YAML/SQL scripting; cloud knowledge of Azure/AWS/GCP and containerization technologies (Docker, Helm, AKS).
Experience with model serving technologies including FastAPI, Docker, AKS; experience with ONNX/TensorRT optimizations; databases like PostgreSQL and Redis.
Experienced in building scalable, production-ready MLOps pipelines with strong automation, monitoring and security practices.
Comfortable working with cloud native tools and services (Azure primarily, but AWS/GCP knowledge is valuable).
Exposure to large language model (LLM) fine-tuning pipelines, Ray Serve or BentoML, and infrastructure automation is a plus.