Applied AI Architect, Meta Enterprise Platform (Singapore)

Meta; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

Meta Singapore is seeking an Applied AI Architect for the Enterprise Platform. You will be the strategic technical advisor guiding enterprise customers from discovery through deployment, shaping architectures for AI products like Model API, and driving ongoing adoption and expansion.

This role combines pre-sales technical leadership with post-sales customer adoption, designing scalable architectures, frameworks, and reference implementations to unlock business value.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent experience
• 5+ years in a technical customer-facing role such as AI Architect or Solutions Architect
• Experience engaging enterprise customers through the sales lifecycle and multi-stakeholder decisions
• Experience with cloud-native architectures, API design patterns, security, and data governance
• Comfortable with Python and building lightweight demos/reference implementations
• Familiarity with Generative AI ecosystems and evaluating AI solutions
• Ability to design evaluation tests and assess quality, latency, cost and reliability
• Experience coordinating customer engagements across evaluation, integration and adoption

Responsibilities

• Design scalable architectures for enterprise AI deployments
• Develop evaluation frameworks and reference implementations
• Serve as the technical authority to build customer confidence
• Own customers' technical success from scoping to live production deployment
• Help customers deploy AI solutions that drive API usage and measurable ROI
• Lead and enable sales teams with technical content and enablement

Skills

Enterprise AI

Pre-sales leadership

Python

LLM APIs

Cloud architecture

Security & privacy

RAG architectures

Vector databases

LangChain

Executive communication

Education

Bachelor's degree in Computer Science or Engineering

Tools

AWS

GCP

Azure

Job description

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.

• 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 with a track record of driving technical wins in competitive enterprise sales cycles
• 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 (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 diverse audiences, including C-suite executives, IT leaders, engineering teams, and non-technical business stakeholders with the ability to translate complex AI/ML concepts for C-suite executives, IT leaders, engineering teams and to 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
• 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)
• Experience with AI observability, evaluation frameworks (evals) assessing quality, latency, cost and reliability, and implementing enterprise-grade security guardrails for AI applications
• Experience creating and delivering technical enablement programs for sales teams, partners, or customer communities
• Experience with AI safety, responsible AI practices, and implementing guardrails for LLM applications in regulated industries
• Familiarity with enterprise security and compliance frameworks (SOC 2, ISO 27001, FedRAMP, HIPAA) in the context of AI deployments
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• 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)