Solution Architect

Robust Intelligence's mission is to eliminate AI Risk. As the world increasingly adopts AI into automated decision processes, we inherit great risk. 

Our flagship product is built to be integrated with existing AI systems to enumerate and eliminate risks caused by unintentional and intentional (adversarial) failure modes. With Generative AI becoming increasingly popular, new vulnerabilities and attacks present a significant threat to AI companies and their consumers. Our Generative AI Firewall provides a safety net against these failure modes.

At Robust Intelligence, we have built a multidisciplinary team of ML Engineers, AI security experts, and software engineers to advance the state of AI security. Together, we're building the future of secure, trustworthy AI.

About The Role

Our growing Customer Success team is looking for an analytical and enterprising Solutions Architect. As an SA at Robust Intelligence, you will drive production-grade implementations at scale with some of the largest and most innovative AI organizations in the world. You will be the trusted advisor to our customers for all things AI infrastructure, guiding them through scoping, deployments, and production MLSecOps integrations.

In partnership with the Customer Success and Sales teams, Solutions Architects lay the infrastructure foundation to enable customers to realize value from their investment in Robust Intelligence. Furthermore, with their deep exposure to the customer experience, SAs partner with Engineering and Product Management to influence product roadmap as the voice of the customer.

As a Solution Architect you will:

• Serve as the technical lead for deployments and integrations in customer environments, both production and non-production
• Propose solutions and architecture on how to deploy and leverage Robust Intelligence products in customer environments and MLOps pipelines
• Produce designs and plans for customer deployments
• Provide White Glove service and deployment assistance to complex, high-value customers
• Regularly optimize the deployment process based on experience and customer feedback
• Collaborate with our Engineering and Product teams to drive meaningful product, deployment, and infrastructure improvements
• Serve as a trusted advisor to customers and partners on general cloud infrastructure and MLSecOps
• Research, troubleshoot, and resolve escalated customer issues
• Implement product or infrastructure changes to address unique customer needs or unforeseen issues
• Execute or assist on Statements of Work
• Partner with Success team to identify product-driven opportunities for expansion and renewal
• Contribute technical content to internal and customer-facing repositories, such as Deployment Guides, Knowledge Base, and FAQs

What we look for:

• At least three years of professional experience as a solutions architect, implementation engineer, technical consultant, or another customer-facing technical role
• Deep knowledge of at least one major public cloud provider (AWS, Azure, GCP)
• Strong experience deploying and managing containers in orchestration systems such as Kubernetes
• Working knowledge of infrastructure and configuration automation tools such as Terraform and Helm
• Working knowledge of at least one scripting or programming language (Python preferred)
• Working knowledge of cloud networking (VPC, TLS, DNS, Load Balancing, Private Link)
• Motivation and ability to learn the Robust Intelligence technology stack
• Self-motivated and excited to work at a startup; comfortable with ambiguity, eager to learn, and able to thrive with little to no direct, daily oversight
• Strong communication skills (business fluency in spoken and written English)
• Ability to travel about 25% of the time to meet with your accounts

Preferred qualifications:

• Expertise in SecOps
• Expertise in MLOps
• Programming experience in Go, Python
• Familiarity with the artificial intelligence ecosystem; familiarity with how data scientists and machine learning engineers develop and deploy AI models
• Knowledge of customer success and sales processes
• Bachelor’s degree (or equivalent) in computer science, engineering, mathematics, statistics, or another quantitative field