AI Solutions Engineer (Raleigh)

Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production. Comfortable working in public Cloud environments (AWS, Azure, GCP). Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn. Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy. Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations. Understanding of GenAI concepts and application evaluation + development lifecycle. Proficiency in a programming language (Python, JS/TS, Java, Go, etc). Strong Communication Skills - Ability to simplify complex, technical concepts. A quick and self learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV. Customer facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles. Prior experience working with applications deployed with Kubernetes. Prior experience demoing technical products to both business and technical audiences.

Work closely with sophisticated ML / GenAI teams. Act as a trusted advisor to customers and build relationships with technical and business stakeholders. Advise on GenAI and ML best practices. Give ML and LLM product demos to technical and business stakeholders. Run strategic business reviews for customers in partnership with the sales team. Interface with pre-sales engineering to gather client goals and KPIs. Partner with product and engineering teams to drive the product roadmap. Spearhead new opportunities within existing accounts to drive expansions.