Solutions Architect, Customer Success - US (Remote)
$160k - $200k • Remote • US - Remote • FullTime
Posted 10d ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Fiddler is seeking a Solutions Architect, Customer Success to ensure clients achieve significant, measurable outcomes from their AI observability investments. In this role, you will act as both a technical expert and a trusted advisor, connecting complex Machine Learning (ML) and Large Language Model (LLM) systems with tangible business value. By guiding customers through the onboarding process, building integrations, and advocating for their needs internally, you will help them deploy trustworthy AI at scale, contributing to Fiddler's growth through successful adoption, renewals, and expansion.
Responsibilities
- Drive successful customer onboarding experiences by architecting and implementing solutions aligned with customer goals and Fiddler's capabilities.
- Serve as the trusted technical partner for customers, building relationships with data science and ML engineering teams to ensure they realize measurable value from Fiddler.
- Champion the customer's voice across Fiddler by leading technical engagements, status syncs, roadmap discussions, and escalation management.
- Become a domain expert in AI Observability, mastering Fiddler's platform and guiding customers on best practices for model transparency, performance monitoring, and compliance.
- Deliver seamless integrations and technical success by writing custom integration code to connect Fiddler with customer data ecosystems.
- Accelerate platform adoption by building and refining integration patterns between Fiddler and common data platforms, workflow tools, and ML infrastructures.
- Uncover and drive expansion opportunities by identifying new ways Fiddler can provide impact through advanced use cases, expanded integrations, or deeper model governance.
Requirements
- Bachelor's degree in Computer Science (AI/ML focus), Statistics, Mathematics, or a related field.
- 5-7+ years of professional experience.
- 2+ years of hands-on experience deploying, monitoring, or maintaining ML models in production environments.
- Excellent communication, presentation, and storytelling abilities, capable of distilling complex technical concepts for diverse audiences.
- Strong organizational and project management skills, with the ability to manage multiple customer engagements simultaneously.