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About the Role
Serve as the strategic technical advisor for enterprise customers, guiding them to understand and leverage the full potential of Enterprise AI products through well-designed architectures. This role combines pre-sales technical leadership with post-sales customer adoption and expansion, focusing on designing scalable architectures, developing evaluation frameworks, and creating reference implementations.
Responsibilities
- Partner with sales teams to conduct deep technical discovery with enterprise customers, translating business requirements and existing technology stacks into clear, technically feasible AI solution architectures and integration plans.
- Establish and maintain technical account plans covering current use cases, adoption milestones, and agreed measures of value, including API usage health, blockers, and expansion opportunities.
- Serve as the primary technical advisor throughout the customer AI and API adoption journey, guiding architecture decisions around large language model selection, retrieval-augmented generation patterns, prompt engineering strategies, agentic workflows, tool use, data pipelines, and enterprise integration points.
- Design and guide customer evaluation frameworks to measure AI performance for specific use cases, creating compelling demos, reference architectures, and proofs of concept that demonstrate feasibility and link results to customer business goals.
- Address enterprise security, compliance, and governance requirements by navigating complex sales lifecycles involving IT, Legal, Security, and executive stakeholders to build technical confidence and accelerate deployment decisions.
- Act as the bridge between customers and internal Product, Engineering, and Product Marketing teams, surfacing field insights, common integration patterns, and product gaps that shape the roadmap and go-to-market strategy.
- Create technical content including architecture guides, best-practice documentation, and industry-specific solution briefs, and enable the broader sales and partner ecosystem with technical training and reusable assets.
- Guide customers through enterprise integration challenges including legacy databases, complex infrastructure, and security constraints, serving as the technical owner for strategic accounts from prototyping through production deployment.
- Contribute to organizational strategy by identifying patterns across customer engagements, setting goals for technical win rates and adoption metrics, and coaching peers on scalable approaches to enterprise AI solution design.
- Travel up to 30% of the time to support customer engagements, executive briefings, and field enablement activities.
Requirements
- 8+ years of experience in a technical customer-facing role such as AI Architect, Solutions Architect, Sales Engineer, Technical Account Manager, or a highly technical Forward Deployed or Solutions Engineering role.
- Experience engaging enterprise customers through the full sales lifecycle and deployment decisions, involving multiple stakeholders across IT, Security, Legal, and executive leadership.
- Experience with cloud-native architectures (AWS, GCP, or Azure), API design patterns, modern deployment concepts, data integration, identity, security and privacy, and enterprise integration approaches such as single sign-on and data governance.
- Experience with the Generative AI ecosystem including APIs, prompt engineering, retrieval-augmented generation architectures, vector databases, and evaluation frameworks, with the ability to build and assess lightweight demos, prototypes, and reference implementations.
- Experience communicating complex AI and machine learning concepts to executive leaders, IT organizations, engineering teams, and non-technical business stakeholders, and independently navigating decisions involving dependencies across multiple customer and internal teams.
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).
- Experience with AI observability, evaluation frameworks assessing quality, latency, cost, and reliability, and implementing enterprise-grade security guardrails and responsible AI practices for large language model applications.
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
- Familiarity with enterprise security and compliance frameworks such as SOC 2, ISO 27001, GDPR, FedRAMP, or HIPAA in the context of AI deployments in regulated industries.
- Experience architecting AI and machine learning solutions at scale in enterprise environments, including model deployment, monitoring, governance, and helping customers move from evaluation to live applications with measurable business value.
- Hands-on experience building applications using AI APIs and frameworks such as LangChain or LangGraph, and demonstrated ability to integrate AI tools to redesign workflows and drive measurable efficiency or quality improvements.
Skills
- Generative AI
- Enterprise Architecture
- Communication Skills
- Cloud-native architectures (AWS, GCP, or Azure)
- API design patterns
- Data integration
- Identity, security and privacy
- Enterprise integration approaches
- Generative AI ecosystem
- Prompt engineering
- Retrieval-augmented generation architectures
- Vector databases
- Evaluation frameworks
- AI observability
- Responsible AI practices
- AI skill development
- Ethical AI practices
- Enterprise security and compliance frameworks
- AI and machine learning solutions architecture
- LangChain
- LangGraph
Experience Level
- 8+ years of experience