Build and maintain automated CI/CD and CT pipelines across AWS and Azure
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG)
Build engineering pipes to securely ingest data from legacy systems into cloud-native MLOps workflows
Implement systemized frameworks for LLM evaluation and traditional ML validation
Deploy real-time monitoring for model drift and quality management
Manage all AI resources using Terraform or CloudFormation
Partner with teams using platforms like Palantir and Databricks
Work with central IT and Security to navigate IAM roles and configurations
Optimize model serving endpoints for high-throughput and low-latency
Establish rigorous version control for model weights and data snapshots
Support automation of feature stores and pipelines
Implement automated scanning and guardrails to prevent data leakage

Bachelor’s degree in Computer Science or a related field required
Master’s degree in a quantitative discipline highly desirable
6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment
Deep, hands-on proficiency in both AWS and Azure
Expert Python, SQL, and PySpark
Extensive experience with Docker and Kubernetes
Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs
A strong understanding of statistical validation and model evaluation metrics

Senior Engineer – LLMOps, MLOps

Key skills