• Full-lifecycle problem solving • Own problems end-to-end: from understanding user pain, through solution design, implementation, release, measurement, and iteration - not just the coding step. • Engage directly with customers and internal domain experts to build deep empathy for the workflows and challenges of cloud operators and FinOps practitioners. • Translate ambiguous problem spaces into clear, thin-sliced increments that can be shipped, measured, and learned from quickly. • Use AI tools daily to amplify your own engineering work - coding, analysis, research, and prototyping. • Design and build AI-powered features as a default approach: intelligent recommendations, automated insights, natural-language interfaces, and predictive capabilities for cloud cost optimization. • Make informed decisions on model selection, prompt engineering, latency/accuracy/cost tradeoffs, and responsible AI considerations as a core part of your engineering practice. • Operate with a bias toward action: prototype rapidly, ship frequently, and validate ideas through real customer usage rather than prolonged planning cycles. • Build experiments and MVPs that generate measurable learning - and use those learnings to decide what to invest in next. • Maintain high engineering standards without letting perfection slow down delivery; know when to take deliberate shortcuts and when to invest in durability. • Build across the full stack - backend services, APIs, data pipelines, and frontend interfaces - whatever the problem demands. • Work with cloud-native billing, usage, and operational data from AWS, GCP, and Azure to build cost optimization and governance capabilities. • Develop solutions that operate across Kubernetes environments, data cloud platforms, and broader multi-cloud infrastructure. • Build state-of-the-art solutions for Generative AI observability and FinOps - enabling customers to understand, monitor, and optimize the cost and performance of their AI/ML workloads across cloud environments. • Take full ownership of the solutions you ship - including reliability, user experience, and measurable outcomes. • Define what success looks like for your work using clear metrics: adoption, activation, workflow improvement, cost savings delivered, and customer-reported impact. • Participate in customer conversations and feedback loops to continuously validate direction and surface new opportunities.

• Full-lifecycle problem solving • Own problems end-to-end: from understanding user pain, through solution design, implementation, release, measurement, and iteration - not just the coding step. • Engage directly with customers and internal domain experts to build deep empathy for the workflows and challenges of cloud operators and FinOps practitioners. • Translate ambiguous problem spaces into clear, thin-sliced increments that can be shipped, measured, and learned from quickly. • Use AI tools daily to amplify your own engineering work - coding, analysis, research, and prototyping. • Design and build AI-powered features as a default approach: intelligent recommendations, automated insights, natural-language interfaces, and predictive capabilities for cloud cost optimization. • Make informed decisions on model selection, prompt engineering, latency/accuracy/cost tradeoffs, and responsible AI considerations as a core part of your engineering practice. • Operate with a bias toward action: prototype rapidly, ship frequently, and validate ideas through real customer usage rather than prolonged planning cycles. • Build experiments and MVPs that generate measurable learning - and use those learnings to decide what to invest in next. • Maintain high engineering standards without letting perfection slow down delivery; know when to take deliberate shortcuts and when to invest in durability. • Build across the full stack - backend services, APIs, data pipelines, and frontend interfaces - whatever the problem demands. • Work with cloud-native billing, usage, and operational data from AWS, GCP, and Azure to build cost optimization and governance capabilities. • Develop solutions that operate across Kubernetes environments, data cloud platforms, and broader multi-cloud infrastructure. • Build state-of-the-art solutions for Generative AI observability and FinOps - enabling customers to understand, monitor, and optimize the cost and performance of their AI/ML workloads across cloud environments. • Take full ownership of the solutions you ship - including reliability, user experience, and measurable outcomes. • Define what success looks like for your work using clear metrics: adoption, activation, workflow improvement, cost savings delivered, and customer-reported impact. • Participate in customer conversations and feedback loops to continuously validate direction and surface new opportunities.

• Full-lifecycle problem solving • Own problems end-to-end: from understanding user pain, through solution design, implementation, release, measurement, and iteration - not just the coding step. • Engage directly with customers and internal domain experts to build deep empathy for the workflows and challenges of cloud operators and FinOps practitioners. • Translate ambiguous problem spaces into clear, thin-sliced increments that can be shipped, measured, and learned from quickly. • Use AI tools daily to amplify your own engineering work - coding, analysis, research, and prototyping. • Design and build AI-powered features as a default approach: intelligent recommendations, automated insights, natural-language interfaces, and predictive capabilities for cloud cost optimization. • Make informed decisions on model selection, prompt engineering, latency/accuracy/cost tradeoffs, and responsible AI considerations as a core part of your engineering practice. • Operate with a bias toward action: prototype rapidly, ship frequently, and validate ideas through real customer usage rather than prolonged planning cycles. • Build experiments and MVPs that generate measurable learning - and use those learnings to decide what to invest in next. • Maintain high engineering standards without letting perfection slow down delivery; know when to take deliberate shortcuts and when to invest in durability. • Build across the full stack - backend services, APIs, data pipelines, and frontend interfaces - whatever the problem demands. • Work with cloud-native billing, usage, and operational data from AWS, GCP, and Azure to build cost optimization and governance capabilities. • Develop solutions that operate across Kubernetes environments, data cloud platforms, and broader multi-cloud infrastructure. • Build state-of-the-art solutions for Generative AI observability and FinOps - enabling customers to understand, monitor, and optimize the cost and performance of their AI/ML workloads across cloud environments. • Take full ownership of the solutions you ship - including reliability, user experience, and measurable outcomes. • Define what success looks like for your work using clear metrics: adoption, activation, workflow improvement, cost savings delivered, and customer-reported impact. • Participate in customer conversations and feedback loops to continuously validate direction and surface new opportunities.

• Design and build backend services in Ruby on Rails and Go that power Assist’s AI-driven capabilities • Develop clean, well-structured APIs that integrate seamlessly with other internal systems and the Algolia dashboard • Architect and maintain system interfaces between backend agents and product surfaces • Take ownership of features from design to production, ensuring reliability, performance, and maintainability • Collaborate closely with product managers, designers, and frontend engineers to turn guidance concepts into real, user-facing impact • Review, debug, and occasionally contribute to TypeScript code to unblock teammates and ensure smooth end-to-end delivery • Improve observability, monitoring, and production stability for critical Assist services • Participate in technical discussions and contribute to shaping how Assist evolves as a core product capability