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AI Solutions Engineer for the Solutions Engineering department. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Arize.
Job Responsibility:
Work closely with some of the most sophisticated ML / GenAI teams in the world
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
Nice to have:
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