DevOps Engineer | SDE - 1 | SDE  - 2

Role: Outbound Lead – Consumer Lifecycle Management

Function: Growth / Product Marketing

Location: Mumbai / Bangalore

Type: Full-time

Compensation: Competitive compensation with the backing of a large-scale ecosystem

Industry: Consumer AI

About Company

The company is building the AI layer for Bharat at India-scale. Backed by partnerships with global tech leaders like Meta and Google, it creates AI that serves the entire Indian user base—across languages, contexts, and daily needs.

The company combines deep India-first AI capability with unmatched India-scale distribution. Its platform-and-product stack is engineered from day one for 100M+ users and 1B-ready constraints on latency, cost, reliability, and safety.

The culture emphasises engineering excellence, strong collaboration, and tangible impact across sectors that matter to India.

Position Overview

As Growth Lead for the company's Consumer Lifecycle Management function, you will own the post-install funnel from onboarding through deep activation and long-term retention. You will design and run a rigorous experimentation engine, optimise High-Value Actions (HVAs) across user lifecycle stages, and collaborate with Product, Personalisation, and Analytics teams to drive measurable DAU/MAU growth at India scale.

Roles & Responsibilities •

Own all outbound channels end-to-end: push notifications, SMS, in-app, email. Build and maintain delivery infrastructure in CleverTap.

 • Design and execute lifecycle campaigns across every stage: onboarding nudges (D1/D3/D5), activation prompts, re-engagement sequences, dormant win-back, churn intervention.

• Run continuous experimentation on outbound axes: channel × send time × content × tone × frequency × sequencing. Maintain a hypothesis backlog — every cycle produces at least one testable hypothesis.

• Analyse every campaign post-execution (delivery funnel, incremental lift vs. control, opt-outs). Build a campaign knowledge base that compounds.

• Coordinate across vertical/category teams (nudge content), GTM (acquisition handoff, campaign calendar), Research (vernacular tone, fatigue signals), and Tech (API integration, template approvals). North Star: Nudge-led activation rate ·

•  Nudge-led repeat rate. Key Metrics: Delivery rate, open/CTR, nudge-to-action conversion, re-engagement rate (dormant → active), cost per re-eng
• Run CRM campaigns at a B2C product with 10M+ MAU in India — where outbound was a retention lever, not just a marketing channel.
• Hands-on CleverTap, MoEngage, or Braze experience: built journeys, segments, and automation rules (not just viewed dashboards).
•  Operated WhatsApp Business API at scale: template approval, delivery economics, regulatory compliance.
•  Wrestled with push notification delivery on Indian Android (Chinese OEMs). Have opinions on maximising reach. 
• Can run campaign A/B tests and explain results in incremental lift and statistical confidence, not open-rate deltas. 
• SQL-proficient. Comfortable with campaign attribution and cohort-level analysis. 
• 3–7 years in lifecycle marketing / CRM / outbound ops. Strongest signal: PhonePe, Meesho, Amazon, ShareChat/Moj, Swiggy, Dream11, or comparable.
•  First 6 Months • FnF → Beta: FnF campaign cadence executing. Simple experiments running (send time, channel, tone).
• Weekly campaign learning reports flowing.
•  Beta → GA: Experimentation engine × GTM cohorts live. Channel mix optimised for GA volume. Regulatory audit passed. Template library ready.
•  GA → GA+60: Nudge-based repeat % measurably improving. Parallel experimentation at scale.
• Outbound personalisation (language, vertical, persona) in production. What Success Looks Like  The campaign engine runs at scale: every campaign has a hypothesis, a control, and a post-read. The knowledge base is the first thing the team checks before designing a new campaign.  Delivery infrastructure is reliable and cost-efficient: WhatsApp reaches
• First 6 Months • FnF → Beta: FnF campaign cadence executing. Simple experiments running (send time, channel, tone).
• Weekly campaign learning reports flowing. • Beta → GA: Experimentation engine × GTM cohorts live. Channel mix optimised for GA volume.
• Regulatory audit passed. Template library ready. • GA → GA+60: Nudge-based repeat % measurably improving. Parallel experimentation at scale. Outbound personalisation (language, vertical, persona) in production.
• What Success Looks Like  The campaign engine runs at scale: every campaign has a hypothesis, a control, and a post-read. The knowledge base is the first thing the team checks before designing a new campaign.
•   Delivery infrastructure is reliable and cost-efficient: WhatsApp reaches who it should, push and SMS is optimised per OEM, circle. High nudge led repeat, reactivation rates.

What We Offer

• Opportunity to build lifecycle growth systems for one of India's most ambitious consumer AI products
• High-ownership, startup-like execution within a large-scale distribution ecosystem
• Direct impact on metrics that matter—DAU/MAU, retention, HVA adoption—at 100M+ user scale
• Fast iteration loops, small pods, and strong cross-functional collaboration
• Competitive compensation with the backing of a large-scale ecosystem