Applied AI Architect, Meta Enterprise Platform (London)

As an Applied AI Architect for the Meta Enterprise Platform, you are the strategic technical advisor who helps enterprise customers understand the full potential of our Enterprise AI products (such as Model API) and design the right architectures to unlock that value. You combine deep knowledge of Generative AI with enterprise architecture expertise and effective communication skills to guide customers from initial discovery through successful deployment, ongoing adoption, and expansion. This role combines pre-sales technical leadership with post-sales customer adoption and expansion. You will design scalable architectures, develop evaluation frameworks, create reference implementations, and serve as the technical authority that gives customers and internal stakeholders the confidence to purchase and deploy. You will own the customer's technical success from the initial scoping phase through to live production deployment, post-sales adoption, and growth., 1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. Travel up to 30% of the time to support customer engagements, executive briefings, and field enablement activities

11. Experience engaging enterprise customers through the full sales lifecycle and deployment decisions, involving multiple stakeholders across IT, Security, Legal, and executive leadership
12. 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
13. 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, 14. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
15. 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
16. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
17. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
18. 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
19. 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
20. 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