Tech Lead, GTM Applied AI & Analytics

LinkedIn•San Francisco, CA

4d•Hybrid

About The Position

This role is based in either our San Francisco, New York, or Chicago offices. 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. As part of the Technology & Product Operations organization, you will build the AI agents and agentic workflows that transform how our Product Experience and GTM organizations operate, turning manual, multi-step operational work into automated, agent-driven systems that reduce friction, accelerate execution, and unlock new ways of working across our member, customer, and GTM experiences. You will architect agentic systems, write production-grade code using AI tools, and mentor operations and analytics colleagues. You will own complex initiatives end-to-end, from prototyping new concepts to deploying scalable production systems, and partner with R&D to leverage our internal platforms and shape their evolution. We are seeking a talented and driven product builder who combines deep familiarity with how GTM teams actually operate with applied expertise in modern AI tools and techniques. You bring sharp product judgment, engineering rigor, and a growth mindset. You are comfortable navigating large, complex, and ambiguous GTM data ecosystems, and you influence cross-functional stakeholders through strong relationship-building and collaboration.

Requirements

2+ years of experience building tools, workflows, or products for GTM functions (Sales, Marketing, Customer Success, Revenue Operations), or embedded directly within a GTM, Sales Ops, or Revenue Operations team.
SQL experience with large-scale data warehouses (e.g., Presto, Trino, Spark SQL).
1+ years of experience with GenAI technologies, LLM APIs, and agent frameworks (e.g., LangChain, LangGraph, LlamaIndex, or equivalent).
1+ years of architecting, building, and deploying AI-powered applications, agents, or automated solutions in production environments.
BA/BS in Computer Science, Statistics, Operations Research, Engineering, or a related quantitative field (or equivalent practical experience).

Nice To Haves

Working knowledge of the lead-to-revenue lifecycle, sales processes, and customer journey.
Experience in software engineering, applied AI, data science, or analytics engineering, with a track record of shipping production systems.
Experience integrating with GTM systems such as Salesforce/Dynamics, Outreach, Gong, Marketo, or Slack
Experience designing evaluations, guardrails, and monitoring for LLM-based systems.
Experience as an "AI builder" or "automation lead" embedded within a GTM, Revenue Ops, or Sales Strategy team at a B2B SaaS company.
Proven ability to lead ambiguous, complex technical initiatives from 0 to 1.
Demonstrated experience influencing technical roadmaps in fast-moving environments.
Resilient, resourceful, and self-directed with a strong bias for action.
Passion for AI with a clear, strategic perspective on applying agentic systems to drive business outcomes.

Responsibilities

Architect & Build: Lead the hands-on design, development, and deployment of agentic workflows, multi-agent systems, and AI-powered applications that automate complex GTM processes (e.g., lead qualification, account research, pipeline reviews, quota planning, and proactive seller alerts) and Product Experience processes (e.g., member friction, customer impact, anomaly detection).
Technical Strategy: Define the agentic AI roadmap and architecture for the Technology & Product Operations org, including key decisions on frameworks, tooling, AI evaluation, and best practices. Partner with R&D to build on our internal platforms and data infrastructure, and provide product feedback that shapes their evolution.
End-to-End Delivery: Write (or co-write with AI) high-quality, production-ready Python and SQL to build and maintain agentic workflows, GTM system integrations, and the automated pipelines that support advanced analytics, and insight-delivery systems.
GTM Stack Orchestration: Develop deep, bi-directional integrations between AI agents and our core GTM toolkit, ensuring our stack operates as a unified, agent-driven system.
Applied AI Integration: Serve as the subject matter expert on applying modern AI, LLMs, and agentic patterns (e.g., tool use, RAG, multi-agent orchestration, evals, human-in-the-loop) to solve GTM business problems in partnership with Operations, Data Science, and Engineering teams.
Technical Mentorship: 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.
Performance, Cost, and Governance: Establish KPIs for every agent you ship (e.g., autonomous resolution rate, time saved per workflow, cost per task), continuously optimize for quality and efficiency, and implement guardrails so agents operate within LinkedIn's responsible AI and data privacy standards.
Executive Storytelling: Translate complex technical concepts and agent behavior into clear, concise, and actionable narratives for senior GTM and Operations leadership.
Cross-Functional Partnership: Collaborate with Product, Engineering, and Data Science teams to operationalize and scale agentic systems from prototype to production, ensuring reliability and measurable business impact across the GTM and Product organization.