1 day ago
Singapore, SingaporeSenior
Responsibilities
- Partner with sales to conduct technical discovery and translate customer requirements into feasible AI architectures and integration plans.
- Own technical account plans, adoption milestones, API usage reviews, blockers, additional use cases, and expansion opportunities.
- Advise customers on LLM selection, RAG, prompt engineering, agentic workflows, tool use, data pipelines, and enterprise integrations.
- Design evaluation frameworks and acceptance criteria for AI quality, latency, cost, and reliability, and create demos, reference architectures, and lightweight proofs of concept.
- Address enterprise security, compliance, governance, and stakeholder requirements across IT, Legal, Security, and executive teams.
- Coordinate with Product, Engineering, and PMM teams to share customer feedback, identify integration patterns, and surface product gaps.
- Create architecture guides, best-practice documentation, solution briefs, training, and reusable technical enablement assets.
- Design, prototype, and plan deployment of customer full-stack applications, RAG pipelines, and custom integrations using AI APIs.
- Guide customers through enterprise security, legacy database, infrastructure, and production integration challenges.
- Provide technical leadership on Generative AI architecture, prompt engineering, model evaluation, and AI security guardrails.
- Travel up to 30% of the time.
Requirements
- 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 highly technical Forward Deployed or Solutions Engineering role.
- Experience engaging enterprise customers through sales, purchasing, and deployment decisions involving IT, Security, Legal, executives, engineering teams, and business stakeholders.
- Experience with consultative selling and driving technical wins in competitive enterprise sales cycles.
- Experience with cloud-native architectures, API design patterns, modern deployment concepts, data integration, identity, security, privacy, SSO, data governance, networking, and enterprise integration.
- Comfort with Python for lightweight demos, scripts, and reference implementations.
- Familiarity with Generative AI APIs, prompt engineering, RAG architectures, vector databases, evaluation frameworks, LLM-based prototypes, and reference implementations.
- Ability to design use-case-specific evaluation tests and assess quality, latency, cost, and reliability against customer goals.
- Experience planning and managing customer technical engagements, resolving blockers, and coordinating customer and internal teams through evaluation, integration, and adoption.
- Ability to communicate complex AI and machine learning concepts to C-suite executives, IT leaders, engineering teams, and non-technical stakeholders.
- Preferred: experience architecting AI/ML solutions at enterprise scale, including model deployment, monitoring, governance, and transition from evaluation to live applications.
- Preferred: hands-on experience building applications with AI APIs such as OpenAI and Anthropic, and familiarity with LangChain, LangGraph, vector databases, RAG, agentic workflows, and tool use.
- Preferred: experience with AI observability, evaluation frameworks, enterprise AI security guardrails, AI safety, responsible AI, risk assessment, bias mitigation, and quality reviews.
- Preferred: familiarity with SOC 2, ISO 27001, FedRAMP, and HIPAA in AI deployment contexts.
- Preferred: experience delivering technical enablement programs and integrating AI tools to improve workflows and measurable business outcomes.
Benefits
- Travel requirement is up to 30% of the time.
Tech Stack
Categories
Solutions Engineering
About Meta
Meta builds social platforms and communication apps—including Facebook, Instagram, WhatsApp, and Messenger—and develops AR/VR hardware and software such as Quest to power immersive computing. It monetizes primarily through advertising tools for businesses, with additional revenue from devices and services, and operates a massive global infrastructure. Founded in 2004 and headquartered in Menlo Park, California, Meta Platforms, Inc. is a public company traded on Nasdaq under the ticker META.