Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS and Azure
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG)
Build engineering 'pipes' to securely ingest and move data from legacy systems
Implement systemized frameworks for LLM evaluation and traditional ML validation
Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption
Manage all AI resources using Infrastructure as Code (IaC)
Partner with teams using platforms like Palantir, Databricks, or Snowflake
Work directly with central IT and Security
Optimize model serving endpoints for high-throughput and low-latency
Establish rigorous version control for prompts and model weights
Automate feature stores and feature engineering pipelines
Implement automated scanning and guardrails to prevent prompt injection and data leakage

Bachelor’s degree in Computer Science or a related field required
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 ecosystems
Expert in Python, SQL, and PySpark
Professional experience with frameworks like LangSmith, Arize Phoenix, or WhyLabs
Understanding of statistical validation and model evaluation metrics
Ability to move at the speed of a startup while collaborating in a large-scale enterprise IT landscape

Senior Engineer – LLMOps, MLOps

Key skills