Shreyas Waghmare

• Led engineering for the enterprise data vertical — processing terabytes of data through high-throughput distributed pipelines for PII/BII detection & removal at scale.
• Designed a reusable RL-environment framework, cutting environment setup time by ~70% and enabling repeatable task setups across 10+ AI trainers.
• Built a Ray-based distributed compute platform for baselining RL tasks, cutting experiment turnaround 3x; internal platforms run hundreds of concurrent experiments.
• LLM-based evaluation pipelines with deterministic graders — reliable scoring at scale with <2% variance across runs.
• Automated validation checks replacing manual review, improving data approval rates by 40%.
• Dockerized environments for external AI partners, adopted across 5+ partner integrations; added auth, structured logging & alerting (PagerDuty/Datadog) to reduce MTTD.
• Architected & shipped the internal task-management platform from scratch within a 4-person team, on schedule.

• Led the in-house CRM from inception — secure auth, reusable permission templates, and lead management (filtering, stage tracking, email, owner assignment).
• Built an AI-powered Sales Training platform (ElevenLabs + Gemini + Deepgram), improving sales productivity 1.5x and cutting ramp-up by 2 weeks.
• Led the Internal Meetings Platform — watermarking and interactive elements (polls, quizzes, surveys) serving 50,000+ active learners.
• Built an admin panel for assisted live-class workflows, cutting instructor onboarding 30% across 200+ instructors.
• Owned critical on-call (DB CPU/IOPS, Sidekiq queue depth); upgraded AWS RDS/Lambda to remove maintenance overhead.

• Led the Scaler Topics CS curriculum — maintained an average article rating of 4.6 across 1000+ articles.
• Managed 4 topics/quarter (Linux, Data Structures, OS, DBMS, React…), set up writer/reviewer hiring, and personally reviewed 25% of published articles.
• Led the USA launch of Scaler Academy — backend & frontend changes for the US region.
• Drove the Scaler Neovarsity initiative with Woolf University — hands-on low-level design & implementation.
• Hardened the platform with expanded Cypress + RSpec coverage and tracking across major flows.

• Product Lead for the mobile app — interactive live-video commerce that doubled (2x) user engagement.
• Led the React Native team — shipped a full iOS + Android app from scratch (React + Golang) with custom native components; integrated Google/Facebook APIs for live streaming to social platforms.
• Deployed Jitsi Meet on EC2 autoscaling — 500 concurrent users/session at 99.9% uptime.
• AWS: Lambda, API Gateway, CloudFront, CloudWatch, EC2, CloudFormation, S3, MediaLive, Route 53.

• Delivered 4 end-to-end software projects with cross-functional teams of 5–15 across data engineering and operations.
• Designed & deployed a field-facing automation tool for a multi-country pharmaceutical client — adopted by 5,000+ field users across 3 countries.
• Built automation scripts boosting operations efficiency, with successful large-scale go-lives.

A low-latency calling platform that picks up, holds a real conversation, and books meetings. Built on Gemini + Deepgram + ElevenLabs, tuned for 15+ concurrent calls without sounding like an IVR.

Stack: Gemini, Deepgram, ElevenLabs, Node.js, Redis, WebRTC

Role: Lead engineer · architecture, latency, orchestration

• <800 ms — avg latency
• 15+ — concurrent calls
• 24/7 — uptime

Architected the in-house CRM that retired LeadSquared in 4 months. Zero-conflict RBAC across 12+ modules, sub-millisecond filtering on 3M+ leads, a no-code workflow builder, and double-digit concurrent A/B tests from day one.

Stack: Next.js, TurboRepo, ElasticSearch, PostgreSQL, React Flow, Kafka

Role: Frontend architecture · search infra · RBAC · workflow engine

• 90% — cost reduction
• 3M+ — leads indexed
• 100% — team adoption

Built the automated call-intelligence layer. Every sales and support call gets transcribed, classified, scored against a compliance policy, and fed back as a coaching nudge. LLMs catch the issues a manual QA pass tends to miss.

Stack: Python, FastAPI, OpenAI, AWS MSK, Postgres, OpenSearch

Role: Pipeline architecture · LLM prompting · review tooling

• 10K+ — calls/month
• 94% — precision flagging
• 3 d — review-to-prod loop

An internal training arena where AI agents role-play as buyers across personas, objections, and stalling tactics. Reps practice live, get scored, and walk into the real call already warmed up.

Stack: Gemini, TypeScript, Next.js, Postgres, pgvector

Role: End-to-end · prompts, eval harness, frontend

• 6 wks — rep ramp-up cut
• 40+ — persona library
• 5x — practice volume

RL environments (single + multi-agent) for computer-use agents, SWE-Bench and Terminal-Bench eval tasks, data pipelines, and the internal AI tools the research team runs on. Founding team — started Oct '25 inside Scaler, workspace spun out Apr '26.

Stack: Python, TypeScript, Playwright, Docker, RL · PPO/GRPO

Role: IC Senior Software Engineer · mentor to junior eng across workstreams

• 50+ — engineers in org
• 3 — research partners
• 7 — workstreams