Applied AI Architect, Meta Enterprise Platform

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. You are the architect of the blueprint - ensuring the right technical foundation is in place before and during implementation. You will not just design architectures; you will help to deploy end-to-end AI solutions that drive actual AI usage such as API consumption and business ROI. You will own the customer's technical success from the initial scoping phase through to live production deployment, post-sales adoption and growth.

Responsibilities

**Technical Discovery & Solution Design** - Partner with sales to deeply understand customer requirements, business context, and existing technology stacks. Translate these into clear, technically feasible AI solution architectures and integration plans **Technical Account Ownership** - Establish technical account plans covering current use cases, adoption milestones and agreed measures of value. Review API usage, technical account health and blockers. Qualify additional use cases and help build the technical case for expansion. Coordinate specialist input where needed and follow through on agreed actions **AI Architecture Advisory** - Serve as the primary technical advisor throughout the customer's AI / API adoption journey. Guide architecture decisions around LLM selection, RAG patterns, prompt engineering strategies, agentic workflow and tool use, data pipelines, and enterprise integration points **Evaluation & Proof of Value** - Design and guide customer evaluation frameworks (evals) to measure AI performance for specific use cases. Create compelling demos, reference architectures, and lightweight POCs that demonstrate feasibility and value. Agree acceptance criteria for quality, latency, cost and reliability, linking evaluation results to customer business goals and a decision on production deployment **Enterprise Trust & Governance** - Address enterprise security, compliance, and governance requirements (SOC2, ISO 27001, GDPR). Navigate complex sales lifecycles involving IT, Legal, Security, and executive stakeholders to build technical confidence **Cross-Functional Collaboration** - Act as the bridge between customers and internal Product, Engineering, and PMM teams. Gather technical feedback, identify common integration patterns, surface product gaps, and contribute field insights that shape the product roadmap and GTM strategy **Thought Leadership & Enablement** - Create technical content including architecture guides, best-practice documentation, and industry-specific solution briefs. Enable the broader sales and partner ecosystem with technical training and reusable assets **Hands-on Solutions** - Act as the primary technical owner for strategic accounts. Assist in the design, prototyping, and deployment planning for customer full-stack applications, RAG pipelines, and custom integrations using our AI APIs. Contribute reference implementations and technical reviews **Rapid Prototyping** - Translate customer business problems into concrete technical plans. Rapidly develop and iterate on functional proofs-of-concept (POCs) to demonstrate immediate value **Production Implementation** - Guide customers through the "Integration Wall." Navigate enterprise security, legacy databases, and complex infrastructures **Technical Advisory** - Serve as a subject matter expert on Generative AI architecture, prompt engineering, model evaluation, and AI security/guardrails for C-level executives and lead developers **Travel** - Flexibility to travel up to 30% of the time

Qualifications

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent experience 5+ years of experience in a technical customer-facing role such as AI Architect, Solutions Architect, Sales Engineer, Technical Account Manager, Enterprise Architect or a highly technical Forward Deployed / Solutions Engineering role Experience engaging enterprise customers through the sales lifecycle, purchasing and deployment decisions, involving multiple stakeholders across IT, Security, Legal, and executive leadership Experience in consultative selling with a track record of driving technical wins in competitive enterprise sales cycles Strong understanding of cloud-native architectures (AWS, GCP, or Azure), API design patterns, modern deployment concepts, data integration, identity, security and privacy, and enterprise integration approaches (SSO, data governance, networking) Comfortable with Python and able to build lightweight demos, scripts, and reference implementations (not expected to write production-grade application code) Familiarity with the Generative AI ecosystem: APIs, prompt engineering, RAG architectures, vector databases, and evaluation frameworks. Ability to use LLM APIs to build and evaluate lightweight demos, prototypes and reference implementations Ability to design use-case-specific evaluation tests and assess quality, latency, cost and reliability against agreed customer requirements and business goals Experience planning and managing customer technical engagements, resolving blockers and coordinating customer and internal teams through evaluation, integration and adoption. A high degree of ownership, and the ability to thrive in highly ambiguous, fast-paced environments Experience communicating complex AI/ML concepts to C-suite executives, IT leaders, engineering teams, and non-technical business leaders while simultaneously working with customer engineering teams Experience architecting AI/ML solutions at scale in enterprise environments, including model deployment, monitoring, and governance. Experience helping customers move from evaluation to live applications with measurable business value Experience creating and delivering technical enablement programs for sales teams, partners, or customer communities Experience with AI observability, evaluation frameworks (evals) assessing quality, latency, cost and reliability, and implementing enterprise-grade security guardrails for AI applications Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Familiarity with enterprise security and compliance frameworks (SOC 2, ISO 27001, FedRAMP, HIPAA) in the context of AI deployments Experience with AI safety, responsible AI practices, and implementing guardrails for LLM applications in regulated industries Hands-on experience building applications using AI APIs (e.g., OpenAI, Anthropic) and familiarity with the Generative AI ecosystem (Vector Databases, RAG architectures, agentic workflows, tool use and frameworks like LangChain/LangGraph)