Key skills
PythonJavaGoAIMachine LearningMLGenAILLMLlamaIndexAgenticPyTorchTensorFlowscikit-learnMLOpsLangGraphAWSAzureGCPKubernetesCommunicationCustomer SuccessSales
About this role
Arize AI is a leading AI & Agent Engineering observability and evaluation platform, empowering AI engineers to optimize their AI systems. The AI Solutions Engineer will act as a trusted advisor to clients, driving business value through technical discussions and product demonstrations while building relationships with stakeholders.
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