Applied AI Architect, Meta Enterprise Platform
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. 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.
Applied AI Architect, Meta Enterprise Platform 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
Minimum 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
Preferred Qualifications:
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)
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$157,000/year to $215,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.