AI Solutions Engineer, EMEA

Our engineering team builds systems that interact with some of the most complex software ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more.

We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. 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.

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.

Note: Even if you do not check every single box, we still encourage you to apply!

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

Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes unlimited paid time off, generous parental leave plan, and others for mental and wellness support.