About the role
Live2.ai is hiring its GTM Engineer to build the system that produces its pipeline. This is not a campaign-running or marketing-ops job. It is a hands-on individual contributor role for someone who builds the machine that finds, qualifies and engages the right accounts around the clock, measures it, and keeps improving it. You report to the Head of Sales/BD in Bengaluru, and your output feeds the company's BDR and AE teams as it expands selling into Europe, the US and Southeast Asia.
What you'll build
- An end-to-end automated GTM flow: account and lead sourcing, enrichment, scoring, routing and outreach, so qualified pipeline keeps moving without manual work
- Signal-based targeting: monitoring for hiring, funding, tech-stack changes and website or content engagement, deciding which signals matter for each ICP segment and how each is acted on
- Always-on outreach: triggered, multi-channel sequences across email and LinkedIn, tied to signal and score thresholds, with personalisation built in
- AI agent workflows: LLMs and agents doing lead research, enrichment, personalisation and first-pass qualification
- Scoring and routing: lead scores driven by behaviour and intent rather than firmographics alone, with routing rules by territory, segment and ownership
- One connected stack: CRM, enrichment, sequencing and AI tools wired together through APIs and no-code tools
- Funnel visibility: dashboards that show what's working, what's stalling and where leads drop off
How you'll work
Pipeline generation is treated as a running experiment. You ship new automated approaches fast, measure them honestly, and rebuild workflows or signals that stall instead of patching them, working closely with the Head of Sales/BD and the BDR/AE team to find the highest-leverage places to automate.
What success looks like
In the first 30 days you map the current GTM stack and funnel, ship your first signal-triggered outreach workflow and put baseline reporting in place. By the end of the first quarter, a share of qualified pipeline should come from systems you built, and sellers should be spending their time on conversations rather than prospecting.
What you bring
Two to five years in GTM engineering, growth automation, RevOps or a technical BDR seat; hands-on work with Clay, n8n, Zapier or Make; experience with signal and intent data (Apollo, ZoomInfo, Clearbit, 6sense); a CRM (HubSpot or Salesforce) plus a sequencer (Outreach, Salesloft); and real builds using Claude or GPT. Exposure to enterprise SaaS sales is a plus.
Pay and perks
Live2.ai does not publish a salary for this role. The careers page lists a competitive salary, ESOPs, family health cover and a hardware and learning budget.
How to apply
Apply on the Live2.ai careers page (https://live2.ai/careers/4-gtm-engineer), or email your CV to careers@live2.ai with "GTM Engineer" in the subject. Include a short note on one automated GTM workflow you built yourself: which tools you used, what signal or trigger drove it, and what it did for pipeline. The process is four steps over about two weeks: an intro call, a practical exercise, a team deep-dive and a founder chat.
What they ask for
- 2–5 years in GTM engineering, growth or marketing automation, RevOps, or a technical BD/BDR role with deep tooling exposure
- Hands-on with automation and enrichment tools such as Clay, Zapier, n8n or Make
- Experience with intent-data and signal platforms such as ZoomInfo, Clearbit, Apollo or 6sense
- Working knowledge of a CRM (Salesforce or HubSpot) and a sequencing tool (Outreach, Salesloft or similar)
- Practical experience building with LLMs such as Claude or GPT for research, personalisation or workflow automation
- Comfortable with APIs and low-code tools: you can build, connect and debug systems yourself without being a software engineer
- A bias toward shipping and testing over waiting for the perfect plan
Nice to have
- Exposure to enterprise SaaS sales motions
Benefits
- Competitive salary
- Meaningful ESOPs
- Health cover for you and your family
- Hardware and learning budget from day one
- Direct line to sales leadership and real autonomy
- Four-step interview process (~2 weeks) with a decision within three working days of the final round
About Live2.ai
Live2.ai (legal entity Woovly India Pvt Ltd, Koramangala, Bengaluru) builds an AI-native social content intelligence and commerce activation platform. It helps global consumer brands monitor social conversation, audit influencer and UGC campaigns, and turn content into commerce through shoppable video and social walls. It says it works with Fortune 500 consumer brands, processes over 1B events a day across 80+ market languages, and holds SOC 2 Type II and ISO 27001. Founded by Neha Suyal (who started Woovly in 2019) and launched in 2024, it grew out of the Woovly social-commerce business. Woovly's backers include Sony Innovation Fund, SOSV, Anthill Ventures, RTAF and ViNners, with about $8.9M raised across several rounds as reported by third-party databases. The company is a small team of roughly 17-50 people headquartered in Bengaluru, and it is hiring sellers for Europe and Southeast Asia.
Original listing
We're hiring a GTM Engineer in Bengaluru. Apply here: https://live2.ai/careers/4-gtm-engineer. Not someone to run campaigns by hand; someone to build the machine that does it for us, around the clock. At Live2.ai, we're building an AI-native social content intelligence platform for Fortune 500 consumer brands. You'll own the system that generates our pipeline: signal-based targeting (hiring, funding, tech-stack changes, web engagement); always-on, multi-channel outreach triggered by intent and score; LLM and AI-agent workflows for research, enrichment and first-pass qualification; behaviour-based scoring and routing, plus dashboards that show where leads stall. You'll fit if you have 2–5 years in GTM engineering, growth automation, RevOps or technical BDR work, and you've built with tools like Clay, n8n, Apollo, HubSpot and Claude or GPT. Send your CV to careers@live2.ai with a short note about one automated GTM workflow you built yourself: which tools you used, what triggered it, and what it did for pipeline.