Job description

This role will be based in San Francisco, Sunnyvale, New York City, or Chicago 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. 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. Basic Qualifications • 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. Preferred Qualifications • 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 Dynamics, Microsoft Fabric, and Gong among others. Suggested Skills • Python • JavaScript • SQL • Machine Learning • Model Development & Deployment LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $141,000 to $231,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit careers.linkedin.com . Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: app.smartsheet.com Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: • Documents in alternate formats or read aloud to you • Having interviews in an accessible location • Being accompanied by a service dog • Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: lnkd.in. Global Data Privacy Notice and Compliance Posters for Job Candidates Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: linkedin.com.

Requirements

Must-haves

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.
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.
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.
Machine Learning
Model Development & Deployment

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 Dynamics, Microsoft Fabric, and Gong among others.
Suggested Skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

goaimlllmmachine learningpythonjavascriptsql

Platform: smartrecruiters
Effort: medium

Benefits

Benefits detected in the listing.

Vision