AI Engineer, AI & Applications
Core
Design, develop, and maintain end-to-end MLOps workflows, model registry strategies, and production quality gates for AI services.
Role type
Senior MLOps Engineer
Builds
Production ML delivery pipelines, monitoring dashboards, and automated evaluation frameworks
Domain
AI Infrastructure & MLOps
Deliverable
infrastructure
Required skills
MLOps, MLflow Model Registry, SLO/SLI definition, model versioning, drift detection, CI/CD, distributed systems, incident response, GPU metrics, RAG evaluation
Preferred skills
LLM-as-judge evaluation, RAGAS, PagerDuty integration, on-call runbook creation
Technologies
MLflow, PagerDuty, Slack, RAGAS, LLM-as-judge
Responsibilities
Own MLflow Model Registry workflows including stage and alias strategies; Establish model and dataset reproducibility and lineage; Develop automated production quality gates; Build monitoring dashboards for model performance and data drift; Create on-call runbooks and incident-response processes; Instrument AI services with request traces and GPU metrics; Integrate benchmarking and evaluation frameworks into CI/CD
Seniority
Senior, hands-on IC
