AI Solutions Engineer

Arize AI is a leading AI & Agent Engineering observability and evaluation platform, empowering AI engineers to optimize their systems. The AI Solutions Engineer will act as a trusted advisor to customers, providing guidance on best practices and conducting product demos to drive business value.

Responsibilities:

• Work closely with some of the most sophisticated ML / GenAI teams in the world
• You will act as a trusted advisor to our customers, while also building 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 our sales team
• Interface with our pre-sales engineering team to gather client goals and KPI’s
• Partner with our product and engineering teams to help drive the product roadmap
• Spearhead new opportunities within existing accounts to help drive expansions

Requirements:

• 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
• Previous engineering experience in Data Science
• MLOps
• ML Frameworks
• LLM / Agentic frameworks
• 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