Ranjith DVL
I'm an AI Product Manager who takes agentic products from a blank page to enterprise adoption. What sets me apart is the combination of engineering depth and product judgment: three years shipping production Python before I moved into product means I can design agent workflows that are technically sound and reason with engineers directly — while owning discovery, PRD authoring, and the metrics that decide whether a product earns its place. I gravitate to 0→1 problems where the path isn't obvious yet, and I measure myself on outcomes that matter to the business — MTTR, defect rates, adoption, and the enterprise 'yes'. Today I'm building an agentic AI platform for SRE teams at Zemoso Labs, where that approach has won two enterprise customers and a paid pilot.
I'm an AI Product Manager with 5+ years spanning full-stack engineering at Wipro and product leadership at Jio Platforms, FanPlay IoT, and Zemoso Labs. I own the full product lifecycle for Agentic AI platforms — from 0→1 discovery and PRD (Product Requirements Document) authoring through enterprise customer pilots — with a consistent record of cutting MTTR (Mean Time To Resolution), defect rates, and user drop-off while winning buy-in from enterprise customers.
Year by year
Four internships (H-Bots, Huawei, Mentor Graphics, Wipro), RoboVITics advisory board, GDG Best Hardware Model award.
2016
2020Cut page latency 89%, auto-remediation cut defect wait 85%, 18-month fast-track promotion & Extra Mile Award.
National wins at Salesforce Futureforce & SIBM Boardroom Challenge, alongside two PM internships.
2023
2024Shipped FANBox end to end; time-to-market down 15%; GTM with Dinesh Karthik lifted acquisition 20%.
Cohort analysis surfaced a 15% drop-off; LLM AI-chat proposal adopted for the Phase 2 roadmap.
2024
20250→1 agentic SRE platform: 2 enterprise buy-ins, 1 paid pilot, 8 core features shipped.
Roles in full
- Secured buy-in from 2 enterprise customers and progressed 1 to a paid Pilot PoC (Proof of Concept) deployment, validating product-market fit for a 0→1 agentic AI platform.
- Defined the end-to-end golden path — from dashboard access to raising a ticket to resolving the alert — designing an evidence-backed hypothesis workflow powered by 5 worker agents.
- Authored PRDs (Product Requirements Documents) and user-journey maps for 8 core features: AI RCA (Root Cause Analysis), alert prioritization, an LLM chatbot, Human-in-the-Loop refinement, and Execute / Raise-Ticket flows.
- Shipped a 0-defect customer demo by leading structured edge-case testing sprints, closing every critical gap before external showcases.
- Automated Jira-to-Spreadsheet progress tracking with Claude Code + n8n, eliminating ~3 hours/week of manual client reporting.
- Identified a 15% user drop-off through DAU/MAU/retention (Daily/Monthly Active Users) cohort analysis and presented a data-backed mitigation roadmap to senior PMs — directly influencing the MyJio engagement backlog.
- Benchmarked 4+ gamification platforms and proposed an LLM-based (Large Language Model) AI Chat integration for MyJio's Play&Win feature — adopted for Phase 2 roadmap planning.
- Surfaced 20+ critical production bugs and 11 feature enhancements through AI-assisted product audits, reducing post-launch incident risk.
- Cut time-to-market by 15% by introducing an Agile sprint structure and phased releases across a 5-person cross-functional team.
- Designed an ML-based (Machine Learning) player performance tracker UX from scratch — personas, wireframes, and data model — after competitive analysis of 10+ sports apps.
- Drove a 20% uplift in user acquisition through a GTM (Go-To-Market) campaign featuring cricketer Dinesh Karthik, pairing gamification mechanics with targeted social distribution.
- Slashed page latency by 89% (39 seconds → 4 seconds) via database task automation, directly improving customer-facing experience for a global enterprise client.
- Reduced defect resolution wait time by 85% through auto-remediation of alarms, measurably improving CSAT (Customer Satisfaction) scores.
- Decreased code complexity by 58% by modularizing a legacy Python monolith into reusable components.
- Engineered ML-driven server-health monitoring, preventing 180+ production server downtimes.
- Delivered ~20K lines of code, 140+ ticket resolutions, and 24 enhancements — earning an 18-month fast-track promotion.
- Received the Extra Mile Award for shipping 3 novel features in 2 months; mentored 4 new joiners.