AI Solutions Architect Jobs

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The Senior Legal Solutions Architect is responsible for designing, building, and scaling AI-native systems that support OpenAI's Legal function, combining traditional legal systems with model-driven automation using OpenAI’s API and agent builder platform. This role involves architecting agentic legal workflows that include multi-step reasoning, tool-calling, orchestration, and human-in-the-loop review. The architect builds systems for triaging legal intake, managing contract and billing data, applying playbooks, flagging issues, and interacting with downstream systems in a controlled way. They define guardrails for autonomy and escalation paths. The architect acts as the primary steward of the legal technology stack, including CLM, OCM, intake systems, workflow orchestration, AI/agent services, and data platforms. They design data flows to ensure legal data is structured, governed, and suitable for analytics and AI use. They enable legal analytics by overseeing data quality, lineage, and auditability. Additionally, they configure, extend, support, and sometimes build API integrations, webhooks, and middleware across legal and enterprise systems. The role emphasizes hands-on architecture, integration, and extension of legal and internal technology platforms to support rapid iteration and scalable legal workflows while maintaining security, reliability, and governance.

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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 design multi-agent systems using different patterns, implement agent logic with frameworks like langchain/langgraph, design evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. The role involves leading technical maturity assessments, working directly with enterprise customers to understand requirements and provide recommendations, and partnering with Engagement Managers and Product/Engineering teams. Responsibilities combine software development, infrastructure/platform engineering, and customer-facing skills focusing on Kubernetes cluster design to multi-agent system architecture to solve real business problems.

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As a Forward Deployed Engineer at Dust, your responsibilities include writing production-quality code to build custom integrations, APIs, and tooling for enterprise customers where off-the-shelf solutions are insufficient. You will contribute features and improvements directly to the Dust platform based on customer requirements and field insights. You act as a key cross-functional partner by collaborating with Sales to help onboard customers and with Customer Success to ensure users maximize the value of Dust. You help set the product roadmap by surfacing feedback and insights from customers, partnering with Design and Engineering. You lead demo calls, communicate Dust's value proposition to buyers and evaluators, and act as a trusted advisor to strategic customers by helping set up their Dust workspace, data connections, AI assistants, and workflows. You identify and highlight successful use cases and craft content to help users maximize Dust's value. Additionally, you lead workshops and training sessions to demonstrate advanced features and facilitate customer access to advanced use-cases through Dust's Developer platform and API.

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The Solutions Architect is responsible for designing, deploying, and optimizing production-grade AI infrastructure and agent systems by architecting scalable, secure infrastructure deployments and building reliable, well-evaluated agent applications that meet real business needs. Responsibilities include infrastructure and platform engineering such as designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region high availability/disaster recovery strategies, and CI/CD pipelines. Additionally, they design multi-agent systems using various patterns, implement agent logic with modern frameworks (langchain/langgraph), design evaluation frameworks, optimize prompts through A/B testing, and guide deployment and operations. The role also entails customer engagement by leading technical maturity assessments, working directly with enterprise customers to understand requirements, presenting recommendations, and partnering with Engagement Managers and Product/Engineering teams.

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As an AI Solution Architect at Mistral AI, the responsibilities include driving the adoption and deployment of Mistral's AI solutions by working closely with customers from strategic vision to production implementation. This involves leading executive-level workshops to identify business challenges and opportunities, co-creating AI adoption roadmaps with customers, and collaborating with Account Executives to develop business cases and align solutions with customer objectives. The role requires architecting end-to-end AI solutions that integrate Mistral's models and platform into customer workflows and infrastructure, partnering with the Applied AI team to design, prototype, and deploy solutions, and overseeing pilot projects and proofs-of-value to demonstrate technological potential. The architect serves as a trusted advisor guiding customers' AI strategies, monitoring KPIs related to business outcomes, and identifying expansion opportunities. Additionally, the role acts as a liaison between customers and internal teams, develops reusable assets and best practices for consistent delivery, and involves travel to foster client relationships and support on-site deployment.

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The AI Solution Architect is responsible for driving the adoption and deployment of Mistral’s AI solutions by working closely with customers from strategic vision to production implementation. This includes leading executive-level workshops to identify business challenges and opportunities, co-creating AI adoption roadmaps, collaborating with Account Executives on business cases, architecting end-to-end AI solutions integrating Mistral's models and platform into customer workflows and technical infrastructure, partnering with the Applied AI team to design, prototype, and deploy AI solutions in production, executing pilot projects and proofs-of-value, serving as a trusted advisor to customers to guide their AI strategy and maximize investment value, monitoring KPIs tied to business outcomes and communicating progress to executive sponsors, proactively identifying expansion opportunities within accounts, acting as a bridge between customers and Mistral’s internal teams to influence product and research roadmaps, developing reusable assets, best practices, and playbooks to scale go-to-market efforts, and traveling approximately 30-60% to foster client relationships and support on-site deployment.

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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 to 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 assist the broader developer community 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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The AI Deployment Engineer serves as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI solutions. They engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in customers' roadmaps. The role accelerates customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. The engineer maintains strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. They contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers. The engineer shares learnings and collaborates with internal teams to inform product development and improve customer outcomes. Additionally, they codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.

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As an AI Deployment Engineer, you will serve as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI (GenAI) solutions. You will engage with senior business and technical stakeholders to identify, prioritize, and validate high-value GenAI applications in their roadmap. Your role includes accelerating customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. You will maintain strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. Additionally, you will contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers, share learnings, collaborate with internal teams to inform product development and improve customer outcomes, and codify knowledge to help the Solutions Architecture team scale impact across industries and customer types.

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The AI Deployment Engineer is responsible for serving as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI solutions. They engage with senior business and technical stakeholders to identify, prioritize, and validate high-value GenAI applications in the customers' roadmaps. The role involves accelerating customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. The engineer maintains strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. They contribute to open-source resources and enterprise-facing technical documentation to scale best practices, share learnings, and collaborate with internal teams to inform product development and improve customer outcomes. Additionally, they codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.