Arize AI
Forward Deployed AI Engineer, West
About this role
Arize AI seeks a Forward Deployed AI Engineer to work directly with enterprise customers on implementing GenAI observability and evaluation solutions. You'll manage multiple concurrent client engagements, build custom integrations, and bridge technical and business needs for Fortune 500 companies.
What you'll do
- Partner with enterprise AI teams to design and scale production-grade GenAI observability programs
- Lead technical discussions with customer stakeholders from engineers to executives
- Build custom integrations and workflows extending the Arize platform for client needs
- Manage multiple concurrent customer projects from scoping through delivery
- Collaborate cross-functionally with Sales, CS, Product, and Engineering teams
- Define and scale best practices and delivery methodologies for the team
What they're looking for
- Software development (2-5 years) in Python, Java, and/or TypeScript
- MLOps pipelines and Generative AI application development
- Cloud platforms (AWS, GCP, Azure)
- Docker and Kubernetes containerization
- Client communication and stakeholder management
- Project management and multi-engagement coordination
- Ability to work in fast-paced startup environments
- AI passion and technical expertise
Benefits
- Competitive salary ($125K-$175K) plus equity package
- Medical, dental, and vision coverage
- 401(k) plan
- Unlimited paid time off
- Generous parental leave
- Mental health and wellness support
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Arize AI
Arize AI builds observability and evaluation solutions for production generative AI systems, helping enterprise customers monitor and secure their AI deployments. The company is hiring Forward Deployed AI Engineers to implement these solutions directly with clients, DevSecOps Engineers to secure AI infrastructure, and AI Sales Engineers to guide enterprise customers through technical evaluations and deployments.
- Website
- arize.ai
Likely interview questions
- Tell us about a time you built and deployed a generative AI application or MLOps pipeline in production. What were the key challenges, and how did you approach monitoring or evaluating its performance?
- Describe your experience working with Python, Java, and/or TypeScript. Which languages are you most comfortable in, and have you used them in ML/AI contexts?