Applied AI Architect, Meta Enterprise Platform

Meta•Austin, TX

8d•$157,000 - $215,000

About The Position

The Applied AI Architect for the Meta Enterprise Platform serves as a strategic technical advisor to enterprise customers, helping them understand Meta Enterprise AI products such as Model API and design architectures that deliver value. The role combines deep Generative AI knowledge, enterprise architecture expertise, and customer communication to guide engagements from discovery through deployment, adoption, and expansion. The architect owns customers’ technical success from initial scoping through production deployment and post-sales growth. This includes designing scalable architectures, developing evaluation frameworks, creating reference implementations, building and iterating on proofs of concept, and helping deploy end-to-end AI solutions that drive API usage and business return on investment. The position combines pre-sales technical leadership with post-sales customer adoption and expansion, and serves as a technical authority for customers and internal stakeholders.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent experience.
At least 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 or Solutions Engineering role.
Experience engaging enterprise customers throughout sales, purchasing, and deployment decisions, including stakeholders in IT, Security, Legal, and executive leadership.
Experience in consultative selling and 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 such as SSO, data governance, and networking.
Comfort with Python and ability to build lightweight demos, scripts, and reference implementations; production-grade application coding is not required.
Familiarity with the Generative AI ecosystem, including 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 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.
High degree of ownership and ability to work effectively in ambiguous, fast-paced environments.
Experience explaining complex AI/ML concepts to C-suite executives, IT leaders, engineering teams, and non-technical business leaders while working with customer engineering teams.

Nice To Haves

Experience architecting AI/ML solutions at enterprise scale, including model deployment, monitoring, and governance, and 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 assessing quality, latency, cost, and reliability, and enterprise-grade security guardrails for AI applications.
Experience applying responsible and ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Ongoing AI skill development, including prompt or context engineering and agent orchestration, and staying current with emerging AI technologies.
Demonstrated ability to integrate AI tools to optimize or redesign workflows and achieve measurable impact, such as efficiency gains or quality improvements.
Familiarity with enterprise security and compliance frameworks such as SOC 2, ISO 27001, FedRAMP, and HIPAA in AI deployments.
Experience with AI safety, responsible AI practices, and LLM application guardrails in regulated industries.
Hands-on experience building applications using AI APIs such as OpenAI or Anthropic, and familiarity with vector databases, RAG architectures, agentic workflows, tool use, and frameworks such as LangChain or LangGraph.

Responsibilities

Partner with sales to understand customer requirements, business context, and technology stacks, then translate them into feasible AI solution architectures and integration plans.
Create technical account plans covering use cases, adoption milestones, and measures of value; review API usage, account health, and blockers; qualify additional use cases and support expansion.
Advise customers on architecture decisions involving LLM selection, RAG patterns, prompt engineering, agentic workflows and tool use, data pipelines, and enterprise integrations.
Design and guide customer evaluation frameworks, demos, reference architectures, and lightweight proofs of concept. Establish acceptance criteria for quality, latency, cost, and reliability, and connect evaluation results to business goals and production decisions.
Address enterprise security, compliance, and governance requirements, including SOC 2, ISO 27001, and GDPR; build confidence across IT, Legal, Security, and executive stakeholders during complex sales lifecycles.
Connect customers with internal Product, Engineering, and PMM teams; collect technical feedback, identify integration patterns and product gaps, and contribute field insights to product roadmap and go-to-market strategy.
Create architecture guides, best-practice documentation, and industry-specific solution briefs; provide technical training and reusable assets for sales and partner teams.
Own technical delivery for strategic accounts, assisting with the design, prototyping, and deployment planning of full-stack applications, RAG pipelines, and custom AI API integrations; contribute reference implementations and technical reviews.
Translate customer business problems into technical plans and rapidly develop and iterate functional proofs of concept.
Guide customers through production integration challenges involving enterprise security, legacy databases, and complex infrastructures.
Advise C-level executives and lead developers on Generative AI architecture, prompt engineering, model evaluation, and AI security and guardrails.
Travel up to 30% of the time.