AI Architect - Business Applications, LinkedIn Marketing Solutions

LinkedIn•Sunnyvale, CA

19h•Hybrid

About The Position

This role will be based in San Francisco, CA, Sunnyvale, CA, New York, NY, or Chicago, IL. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. The Tech & Analytics team builds the analytical and automation foundation that powers LinkedIn's most important Go-to-Market decisions. We partner across Sales, Customer Success, Marketing, and Engineering to create a unified understanding of GTM performance. Our mission is to transform data into proactive insights and intelligent systems that guide LinkedIn's growth and efficiency. We're hiring an AI Architect to design, build, and launch AI-powered features inside the business applications our employees use every day. You'll work as a product-minded software builder — turning LLMs, agents, and AI-driven automation into reliable, well-scoped features that meaningfully improve how our internal teams get work done. This is a hands-on builder role. You'll own features end-to-end: from problem framing and prototyping, through evaluation and rollout, to monitoring in production. You'll partner closely with product managers, data engineers, and platform teams to ship AI capabilities that are ready to support an enterprise sales organization.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field, OR equivalent practical experience.
5+ years of professional software engineering experience building and maintaining production applications.
5+ years of experience building or contributing to enterprise or business applications (line-of-business tools, internal platforms, workflow systems, or similar).
1+ years of experience with GenAI technologies and frameworks (e.g., LangChain, LLM APIs).
1+ years of architecting, building, and deploying machine learning models and/or automated data solutions in production environments.

Nice To Haves

Experience defining and applying AI evaluation strategies, prompt engineering techniques, and safety/guardrail patterns in real systems.
Strong knowledge of responsible AI practices: handling sensitive data, access control, PII, abuse mitigation, and human-in-the-loop patterns.
Experience with modern GenAI frameworks and tooling (e.g., LLM APIs, orchestration frameworks, agent frameworks, vector stores, RAG pipelines).
Experience operating software at scale — designing for reliability, performance, and cost in production.
Experience with observability, evaluation, and experimentation tooling for AI features (offline evals, online A/B testing, tracing, feedback loops).
Comfort working across the stack as needed (backend services, APIs, and front-end integration) to deliver a complete user experience.
Strong product instincts: a sense for what makes an AI feature genuinely useful versus merely impressive.
Experience with enterprise business applications including Salesforce, Tableau, Gong, and Pigment among others.

Responsibilities

Define the technical roadmap and architecture for Technology & Product Operations org, including key decisions on frameworks, tooling, and practices.
Partner with R&D to build applications leveraging our internal platforms, as well as provide input to R&D on enhancements to our technical platforms and data infrastructure
Lead the hands-on design, development, and deployment of scalable data products, AI/ML models (e.g., member friction, customer impact, anomaly detection), and GenAI-powered agentic workflows.
Serve as the subject matter expert on applying modern AI, LLMs, and ML techniques (e.g., RAG, fine-tuning) to solve GTM business problems within Enterprise Applications in partnership with Operations, Data Science and Engineering team
Design for quality and trust: define evaluation criteria, build active monitoring, implement safe use guardrails, and continuously measure AI feature performance against business outcomes.
Mentor operations and analytics colleagues on AI tooling and applications, setting a high bar for technical rigor, code quality, and engineering best practices through a lead-by-example approach.
Operate at scale and in production: instrument features to minimize latency and cost, maximize reliability and accuracy; debug failure modes; iterate based on real usage.
Collaborate with Product, Engineering, and Data Science teams to operationalize and scale models from prototype to production, ensuring reliability and measurable business impact.
Translate complex technical concepts and model outputs into clear, concise, and actionable narratives for non-technical stakeholders and senior leadership.