AI Engineer - Forward Deployed Engineer Associate Manager - Databricks - Reinvention Centers

Responsibilities: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability. Build RAG and multi-agent systems on Databricks using Mosaic AI, Vector Search, Model Serving, and MLflow; govern data and models through Unity Catalog. Develop data and feature foundations on the Lakehouse (Delta Lake, DLT, Unity Catalog) that feed production AI applications. Tailor and deploy agentic applications across industries such as finance, healthcare, and retail. Conduct design workshops, proofs of concept, and code-with sessions with client stakeholders. Define and use metrics to measure agent accuracy, latency, safety, and cost effectiveness. Process large-scale distributed datasets on the Databricks Intelligence Platform with Apache Spark™.Integrate LLM solutions with APIs, model monitoring, and prompt management. Operate MLOps / LLMOps pipelines with CI/CD across the ML and LLM lifecycle. This is a hybrid role in Dallas, TX and requires 3 days per week in the office. May consider qualified applicants in Columbus, OH; Tampa, FL; Atlanta, GA; Houston, TX.Travel may be required for this role. The amount of travel will vary from 25% to 75% depending on business need and client requirements.

Here's What You Need: Minimum 4 years of engineering experience on Databricks and large-scale big data projects (Delta Lake, DLT, Apache Spark, Unity Catalog). You've also built API layers that expose data and AI/ML capabilities to enterprise systems. Minimum 5 years of experience in Python, Java, or equivalent. Comfortable with evaluation tooling, logging, monitoring, and observability. Minimum 1 year experience in designing and shipping agentic AI solutions in production. Minimum 1 year experience in shipping agentic solutions in production (agents, orchestration, context engineering, RAG, workflows) using AI platforms (OpenAI, Claude, Vertex AI, open source) and Databricks AI tooling (Mosaic AI, Vector Search, Model Serving, MLflow).Minimum 2 years experience in shipping to production: CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging. Practical MLOps and LLMOps experience across the ML and LLM lifecycle (model training, serving, monitoring, prompt management).Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate Degree, must have minimum 6 years work experience)