L

LinkedIn

Runs a social network for careers and hiring

AI Architect - SaaS GTM Tech & Analytics

AI Application EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
  • At least 5 years of professional software engineering experience building and maintaining production applications.
  • At least 5 years of experience building or contributing to enterprise or business applications, such as line-of-business tools, internal platforms, or workflow systems.
  • At least 1 year of experience with generative AI technologies and frameworks, such as LangChain or large language model APIs.
  • At least 1 year of experience architecting, building, and deploying machine learning models and/or automated data solutions in production environments.

Responsibilities

  • Define the technical roadmap and architecture for the Technology & Product Operations organization, including key decisions on frameworks, tooling, and practices; partner with R&D to build applications using internal platforms and provide input on enhancements to technical platforms and data infrastructure.
  • Lead hands-on design, development, and deployment of scalable data products, AI and machine learning models, and generative AI-powered agentic workflows.
  • Serve as the subject matter expert on applying modern AI, large language model, and machine learning techniques, including retrieval-augmented generation and fine-tuning, to GTM business problems within Enterprise Applications, in partnership with Operations, Data Science, and Engineering.
  • 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 standards for technical rigor, code quality, and engineering practices by example.
  • Instrument features to minimize latency and cost and maximize reliability and accuracy; debug failure modes and 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.

Desired Qualifications

  • Experience defining and applying AI evaluation strategies, prompt engineering techniques, and safety/guardrail patterns in real systems.
  • Strong knowledge of responsible AI practices, including handling sensitive data, access control, personally identifiable information, abuse mitigation, and human-in-the-loop patterns.
  • Experience with modern generative AI frameworks and tooling, including large language model APIs, orchestration frameworks, agent frameworks, vector stores, and retrieval-augmented generation pipelines.
  • Experience operating software at scale, including designing for reliability, performance, and cost in production.
  • Experience with observability, evaluation, and experimentation tooling for AI features, including offline evaluations, online A/B testing, tracing, and feedback loops.
  • Comfort working across the stack as needed, including backend services, APIs, and front-end integration, to deliver a complete user experience.
  • Strong product instincts and the ability to distinguish genuinely useful AI features from features that are merely impressive.
  • Experience with enterprise business applications, including Dynamics, Microsoft Fabric, and Gong.
  • Python.
  • JavaScript.
  • SQL.
  • Machine learning.
  • Model development and deployment.

About the Company

LinkedIn

Runs a social network for careers and hiring

LinkedIn runs a social network for working life, where people keep an online record of their careers and look for jobs, and where employers recruit, advertise, and sell to professionals. It became the world's largest professional network, and in 2016 Microsoft bought it in what was then Microsoft's biggest acquisition. Members fill profiles with their work history and skills, post in a news feed, and apply for openings, while recruiters search those profiles and message candidates through LinkedIn Recruiter. LinkedIn charges employers for these hiring tools and for ads aimed by job title or industry, and members pay for Premium subscriptions. Reid Hoffman co-founded LinkedIn in 2002, and it is based in Sunnyvale, California.

Company size
10,001+
Company stage
IPO
Headquarters
Sunnyvale, CA
Founded
2002

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