AI Solutions Architect Jobs

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Lead post-sales implementations end-to-end including discovery, design, build, UAT, launch, and hypercare, owning scope, delivery, customer enablement, and business impact. Build and integrate production AI agents with customer systems via REST/GraphQL, webhooks, and events; handle authentication such as OAuth2/JWT, data mapping, and robust error handling. Configure agent workflows, prompts, tools, and retrieval/RAG; establish evaluation, guardrails, and reliability standards for quality and safety. Develop lightweight Python automations and custom connectors/middleware to meet integration needs. Set up observability including logging, metrics, tracing, and alerting, and create clear runbooks, playbooks, and technical documentation. Provide Tier 2/3 troubleshooting and root-cause analysis; drive durable fixes and continuous improvement post-go-live. Enable and train customers to be self-sufficient builders on the Relevance AI platform through workshops, onboarding, co-development sessions, and best practices sessions. Act as a strategic partner by channeling customer insights to Product, influencing roadmap and reusable solution patterns. Identify expansion opportunities with Customer Success based on delivered value and measurable outcomes. Support implementation efforts pre-sales (approximately 20%) with targeted discovery, use case selection, and implementation planning.

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The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They also design multi-agent systems using different patterns, implement agent logic with modern frameworks (langchain/langgraph), create evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. Additionally, they lead technical maturity assessments, work directly with enterprise customers to understand requirements and offer recommendations, and collaborate with Engagement Managers and Product/Engineering teams.

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Own configuration, administration, and ongoing maintenance of our AI-powered application platform. Collaborate with infrastructure engineers to shape the framework and standards that govern how AI-assisted applications are built, reviewed, and deployed. Design and build tooling that enables teams to develop and iterate on applications with greater independence and less engineering overhead. Evaluate AI-generated code for security, scalability, and production readiness, and define code review standards specific to AI-assisted development workflows. Serve as a technical advisor to non-technical stakeholders — helping them understand what is possible, where risk lives, and when engineering involvement is required. Establish and promote best practices for AI-assisted development across the engineering organization. Partner with the AI Solutions team and engineering leadership to define the long-term architecture of this platform as it scales.

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The Senior+ Solutions Engineer at Crusoe Cloud is responsible for leading technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers, owning the proof of concept (PoC) process through to post-sales optimization. They architect and deploy ML workloads using Kubernetes-based stacks such as Ray and Kubeflow, design infrastructure focusing on performance, scalability, and efficiency, and deploy and optimize AI/ML workloads directly on Crusoe infrastructure ensuring performance at the container and hardware level. They assist customers in migrating and adapting workloads across AWS, Azure, and GCP, and explain tradeoffs between cloud-native and Crusoe-native approaches. Additionally, they conduct workshops, live demos, and solution reviews, contribute to case studies, solution briefs, and blog posts highlighting customer success, and act as a voice of the customer by relaying feedback to internal engineering and product teams to improve Crusoe’s platform based on real-world implementation experience.

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The Partner AI Deployment Engineer for AWS serves as the senior technical counterpart to AWS field leadership, building trust and credibility while influencing joint account strategy and technical direction for high-priority opportunities. The role involves shaping engagement models, defining prioritization frameworks, and establishing best practices for AWS collaborations. Responsibilities include leading technical strategy for large, complex enterprise engagements from ideation through architecture design, prototyping, and production deployment, acting as a technical decision-maker and escalation point to de-risk implementations. The engineer designs and communicates end-to-end AI architectures using OpenAI and AWS services, builds and guides development of prototypes and reference implementations, and ensures solutions are scalable, secure, and production-ready. They enable AWS and partners via scalable technical motions such as workshops, playbooks, and demos, develop reusable solution assets deployable independently by AWS teams, mentor partner technical teams to achieve self-sufficiency, and extend impact through GSIs, RSIs, and ISVs. Additionally, the role partners with various internal functions to align strategy and execution, acts as a bridge between field and product teams delivering insights to inform roadmaps, and contributes to internal knowledge systems defining standards and playbooks for the AI Deployment Engineering function.

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The Partner AI Deployment Engineer role involves serving as the primary technical counterpart to AWS field leadership, shaping strategy, defining engagement models, and building repeatable systems that scale across AWS globally. Responsibilities include influencing joint account strategy and technical direction for high-priority opportunities, shaping how OpenAI engages with AWS, identifying new opportunities within the AWS ecosystem, and leading technical strategy for large and complex enterprise engagements. The engineer guides AWS and partner teams from ideation through architecture design, prototyping, and production deployment while acting as a technical decision-maker and escalation point. They design and communicate end-to-end AI architectures using OpenAI and AWS services, build prototypes and reference implementations, and establish best practices for scalable and secure GenAI systems. The role includes enabling AWS and partners through workshops, playbooks, and reusable solution patterns, mentoring partner technical teams to accelerate self-sufficiency, scaling impact through GSIs, RSIs, and ISVs, and closely partnering with alliances, product, engineering, GTM, and enablement teams to align strategy and execution. Additionally, the engineer contributes to internal knowledge systems and helps define standards and playbooks for the AI Deployment Engineering function.

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As the Head of Solutions Architecture, you will lead a global team of Solutions Architects to drive sales and revenue growth by designing and delivering AI solutions for enterprise customers. Responsibilities include leading the development and execution of technical sales strategies, building and mentoring the solutions architecture team, owning the sales pipeline as a technical expert and trusted advisor, architecting scalable and secure AI solutions tailored to enterprise challenges, collaborating with product teams to align platform development with customer needs, defining best practices for AI deployment, optimizing sales processes from proposal to onboarding, leading technical engagements with executives, overseeing deployment and integration of AI solutions, mentoring the team to foster technical excellence, collaborating across functions to ensure alignment, cultivating technical champions within customer organizations, partnering with sales leadership on global strategies, and tracking key sales metrics such as pipeline health and revenue contribution.

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Serve as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy GenAI solutions. Engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in their roadmap. Accelerate customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. Maintain strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. Contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers. Share learnings and collaborate with internal teams to inform product development and improve customer outcomes. Codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.

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Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of your development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

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Co-architect and co-build production AI agents with customer engineering teams; own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations; help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows; advise customers post-sale on architecture, best practices, and roadmap-level decisions; run technical demos, trainings, and workshops for developer audiences; surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers; occasionally contribute code upstream when it meaningfully improves customer outcomes.