Okay, I've searched for full-time Customer Solutions Architect roles in the United States requiring AI technology experience, Python skills, cloud infrastructure knowledge, and offering vision insurance benefits. I have found 37 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Customer Solutions Architect at Braintrust is fundamentally hired to bridge the gap between complex AI evaluation platform technology and customer success in deploying and operating it reliably in real-world production environments. This role requires balancing deep technical expertise—especially in cloud infrastructure, DevOps tools, and programming—with the nuanced ability to guide customers through implementation challenges, infrastructure troubleshooting, and scalable solution design. The candidate will frequently navigate intricate issues spanning software development kits, cloud deployments, and large language model (LLM) evaluation, ensuring the platform integrates seamlessly and operates robustly. Success entails not only technical problem-solving but also proactively empowering customers with best practices while translating their needs and challenges back into actionable insights for product and engineering teams. An ability to work autonomously within a fast-paced, small team environment, communicating effectively across technical and non-technical stakeholders, is critical. The job demands a blend of hands-on coding/debugging, systems architecture consultation, customer empathy, and collaboration, placing emphasis on reliability, maintainability, and security of AI evaluation workflows at scale.

Company Analysis:

Braintrust is positioned as an innovative and specialized enterprise-grade platform focused on solving a core, emerging challenge in AI: trustworthy evaluation, monitoring, and deployment of production AI models, particularly those powered by LLMs. As a relatively small but senior team at the cutting edge of AI reliability, Braintrust likely values agility, technical excellence, and strong customer-centricity. The company culture appears to prioritize rapid iteration, technical ownership, and cross-functional collaboration while fostering a mission-driven environment around AI quality. This context means that the Customer Solutions Architect role will be highly visible and influential, operating at the intersection of product, engineering, and customer success functions. The role supports Braintrust’s strategy to expand market adoption by ensuring customers not only deploy the platform but do so with confidence, stability, and operational effectiveness. Candidates should expect a fast-moving startup atmosphere that demands initiative, clear communication, and a passion for AI's real-world impact. Alignment with Braintrust’s core values—innovation, collaboration, and trustworthiness—is essential for long-term fit and impact.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to troubleshoot a complex technical issue for a customer involving multiple system layers. How did you approach it and what was the outcome?
• Behavioral: Describe a scenario where you had to explain a complicated technical concept to a non-technical stakeholder. How did you ensure they understood and what strategies did you use?
• Behavioral: Share an experience where you had to balance competing priorities between customer needs and internal engineering constraints. How did you manage expectations and deliver results?
• Behavioral: Can you give an example of when you took initiative to improve a process or technical artifact that enhanced customer onboarding or operational stability?
• Technical/Role-Specific: How would you design an end-to-end evaluation pipeline for an LLM-powered application using Braintrust’s SDKs and APIs? What architectural considerations would you emphasize?
• Technical/Role-Specific: Walk me through your experience with troubleshooting production deployments on cloud platforms like AWS or GCP, particularly involving container orchestration tools such as Kubernetes.
• Technical/Role-Specific: What best practices would you recommend to ensure security and maintainability for self-hosted AI evaluation environments?
• Technical/Role-Specific: Could you explain how you would use observability tools and monitoring pipelines to proactively detect and resolve reliability issues in a production AI system?
• Situational: Imagine a customer’s deployment is intermittently failing and the root cause is unclear, spanning cloud infrastructure, model outputs, and SDK usage. How would you methodically isolate and resolve the problem?
• Situational: Suppose you receive conflicting feedback from sales and engineering teams about a customer’s feature requirements causing friction. How would you mediate the situation and align all parties?
• Situational: If Braintrust releases an upgrade that introduces breaking changes for self-hosted customers, how would you handle the upgrade support and communication with affected customers?
• Company Fit/Motivation: What excites you about Braintrust’s mission to build trust in AI through evaluation and monitoring? How do you see yourself contributing to that mission?
• Company Fit/Motivation: Given Braintrust’s focus on cutting-edge AI teams and fast-paced innovation, how do you stay current with AI technology trends and ensure your expertise remains relevant?
• Company Fit/Motivation: How do your personal values align with Braintrust’s emphasis on reliability, transparency, and customer-centric solutions in AI production workflows?
• Candidate Questions: Can you describe the typical customer profile and the most common technical challenges they face when adopting Braintrust?
• Candidate Questions: How does the Field Engineering team collaborate with product and engineering to influence the platform roadmap and prioritize customer-driven features?
• Candidate Questions: What are the key success metrics for this role in the first six months, and how is performance typically evaluated?