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Tech Lead Manager - AEI, Argentina

External
profound logoProfound · Buenos Aires, Argentina
$125K–$165K/yrFull-timeOn-site1d ago
FigmaLeadershipLeanLLMsMoveREST
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Responsibilities

  • Lead the Answer Engine Insights roadmap, partnering with the CTO, Head of Engineering, and product leadership on priorities
  • Develop a team of 8-10 engineers, including running interview loops, calibrating performance, and setting expectations clearly
  • Ship code yourself on the highest-leverage parts of the system
  • Be the engineering point of contact for enterprise customers when Answer Engine Insights issues escalate
  • Represent Answer Engine Insights in conversations with sales, customer success, and the executive team when data coverage, platform integrations, or product capabilities affect commitments

Requirements

  • 7+ years of backend engineering experience, with at least 2 years managing or tech-leading a team
  • Deep, hands-on experience with data pipelines and analytics infrastructure; you've built systems that ingest, process, and aggregate large volumes of structured and semi-structured data and can reason about the tradeoffs between latency, cost, and accuracy
  • Experience scraping the web at scale; you are comfortable bypassing bot detection, working with mobile/residential proxies, and reverse engineering web systems
  • Strong product sense for B2B SaaS; you understand what enterprise teams need from analytics products and can design experiences that go beyond dashboards to drive action
  • Strong Postgres + Clickhouse skills; you can reason about indexes, query plans, time-series data models, and migrations on analytics-heavy workloads
  • Experience building frontend analytics experiences - you know what makes a data product feel good to use
  • Experience hiring senior ICs and giving direct, useful feedback
  • Comfortable being an IC contributor on the team you manage; we don't want a manager who has stopped writing code
  • Location
  • This is an on-site role based in our Buenos Aires office, designed for builders who thrive on speed, iteration, and meaningful impact.
  • Note: All official communica

Benefits

Equity / stock options

Additional Information

Profound is the marketing platform for the AI era. As people increasingly turn to ChatGPT, Perplexity, and Gemini to decide what to buy, we give brands the intelligence to see how AI represents them and the Agents to act on it. Today, ~13% of the Fortune 500, plus companies like Ramp, Figma, Chime, Calendly, and DocuSign, use Profound to turn AI Search from a black box into a measurable growth channel. Backed by Lightspeed, Sequoia, Kleiner Perkins, and Khosla Ventures at a $1B valuation, we're a lean, fast-moving team across NYC, SF, Buenos Aires, and London, shipping at a relentless pace and defining a new category at the biggest shift in marketing in 25 years. If you want to do the best work of your career at the frontier of AI, come build it with us. We are hiring a Tech Lead Manager to lead our Answer Engine Insights team in Buenos Aires. Answer Engine Insights is Profound's core analytics product, which gives brands insight into how AI platforms represent them, who's winning in their category, and which sources are shaping those narratives. This is the team that decides how we collect and analyze AI model responses at scale, how we extract competitive signals, how we model visibility/sentiment/accuracy over time, and how we surface all of it in a fast, reliable product experience. The work is high-leverage, deeply cross-functional, and directly tied to revenue. This is a hybrid role. You'll spend roughly half your time on technical leadership and architecture, and half on people management, hiring, and execution. We expect you to write code, review PRs, and be in the details on critical decisions. We also expect you to grow engineers, run a tight planning process, and own outcomes for the team. The Answer Engine Insights team owns prompt management and execution (the systems that run prompts across ChatGPT, Gemini, Perplexity, etc. at scale, including configuration, scheduling, and scraping); response analysis (the analytics layer that extracts named entities, sentiment, and accuracy from LLM responses); and data querying (the query layer responsible for efficiently fetching and aggregating data via dashboards, REST API, and MCP).


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