# Surya Teja Reddy Dwarampudi

> SDE2 · EVARATUS AI · Bengaluru, IN · open to conversations.
> Contact: hi@itssurya.com.
> Currently: building RL environments, authoring SWE-Bench tasks, wiring up data pipelines, building research tools.

## About

**A little background.**

I studied Computer Science at Bennett University, graduating in 2024 with a** 9.8/10 CGPA** and a place in the top 1% of my class.

I started at Scaler as a software engineering intern in 2023. My work since then has taken me from mentor scheduling and sales tools to environments for training AI agents.

- **50+ eng** — engineering organization
- **$10M+** — revenue generated
- **₹5.4 cr+** — annual CRM savings
- **9.8/10** — cgpa · top 1%

## Experience

### Software Engineer II — Evaratus AI (formerly Scaler AI Labs) (current)
_Feb 2026 to present · Bengaluru_

- Build training environments and evaluation infrastructure as part of the founding engineering team.
- Work with **3+ frontier AI labs across the world** on model evaluation across **more than 5 workstreams**.
- Develop data pipelines and internal research tools; mentor junior engineers across the team.

### Software Engineer I — Scaler by InterviewBit
_Jul 2024 to Jan 2026 · Bengaluru_

- Built sales and operations platforms, including an in-house CRM that replaced LeadSquared with **100% adoption in 4 months**.
- Owned frontend architecture, Elasticsearch search, role-based access control across **12+ modules**, and a React Flow workflow builder.
- Developed voice agents, automated call auditing, and an AI simulator for sales training. Project details and results are below.

### Software Engineer Intern — Scaler by InterviewBit
_Mar 2023 to Jul 2024 · Bengaluru_

- **Needle-Mover Award**: reduced mentor no-shows from **11% → 3%** via a custom scheduling feature on Ruby on Rails + React.
- **Self-serve admin tools** cut weekly support tickets by **60%** (30→12), saving **100+ eng-hours/yr**.
- Improved the Article page for **500K+ MAU** using lazy loading and server-side pagination.

### Full-Stack Developer Intern — SCSET · Bennett University
_Sep 2022 to Jan 2023 · Greater Noida_

- Built interactive admin dashboards in Django to visualize inter-school activity data.

## Projects

### 01. Real-time voice agent for sales calls
_flagship · 2025_

Built a calling platform that handles sales conversations and books meetings. Combined Gemini, Deepgram speech recognition, and ElevenLabs speech synthesis, with Redis and WebRTC for call orchestration.

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

**Role:** Lead engineer · architecture, latency, orchestration

**The task:** Handle sales conversations and meeting booking through a real-time voice agent.

**What I built:** Led the architecture, latency work, and call orchestration. Combined Deepgram speech recognition, Gemini, and ElevenLabs speech synthesis with Redis and WebRTC.

**The result:** Average latency below 800 ms, more than 15 concurrent calls, and round-the-clock operation.

- **<800 ms** — average latency
- **15+** — concurrent calls
- **24/7** — operation

### 02. In-house CRM saving ₹5.4 Cr+ a year
_scale · 2024_

Replaced LeadSquared with an internal CRM, cutting annual costs by 90%. Built lead filtering with sub-millisecond search latency across 3M+ leads and 100K+ monthly queries, role-based permissions, a workflow builder, and support for concurrent A/B tests.

**Stack:** Next.js, Turborepo, Elasticsearch, PostgreSQL, React Flow, Kafka

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

**The task:** Replace LeadSquared with an internal CRM for the sales team.

**What I built:** Owned frontend architecture, search infrastructure, role-based permissions, and the workflow engine. Built filtering over 3M+ leads, permissions across 12+ modules, a React Flow workflow builder, and support for concurrent A/B tests.

**The result:** Reduced annual costs by 90%, saving ₹5.4 Cr+ per year. Reached full team adoption in four months. Search served 100K+ monthly queries with sub-millisecond latency.

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

### 03. Call audit pipeline at 10K calls/month
_compliance · 2024_

Built a pipeline that transcribes sales and support calls, checks them against compliance policies, and sends flagged passages to reviewers. Added review tooling and coaching feedback, with 94% precision on flagged issues.

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

**Role:** Pipeline architecture · LLM prompting · review tooling

**The task:** Check sales and support conversations against compliance policies and surface passages for human review.

**What I built:** Built the transcription and audit pipeline, LLM prompting, review tooling, and coaching feedback screens.

**The result:** Processed 10K+ calls per month with 94% precision on flagged issues and a three-day review-to-production loop.

- **10K+** — calls/month
- **94%** — precision on flagged issues
- **3 d** — review-to-prod loop

### 04. A practice ground for sales conversations
_training · 2025_

Built a training platform where sales reps practice conversations with AI prospects. Created a library of 40+ personas, an evaluation harness, and feedback screens. The platform increased practice volume fivefold and cut ramp-up time by six weeks.

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

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

**The task:** Give sales reps a place to practice conversations with AI prospects.

**What I built:** Built the platform end to end: persona prompts, an evaluation harness, and the frontend for practice and feedback. Created a library of more than 40 personas.

**The result:** Practice volume increased fivefold and rep ramp-up time fell by six weeks.

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

### 05. Agent training at Evaratus AI (current)
_current · founding · 2026–_

Build GUI environments in which single and multi-agent systems can perform desktop tasks. Author SWE-Bench and Terminal-Bench evaluations, process task traces into training data, and develop tools for the research team.

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

**Role:** Software Engineer II · environments, evaluation, research tooling

**The work:** Build GUI environments in which single and multi-agent systems can perform desktop tasks.

**My contribution:** Author SWE-Bench and Terminal-Bench evaluations, process task traces into training data, and develop internal research tools. I also mentor junior engineers.

**Team context:** Part of the founding team at Evaratus AI, formerly Scaler AI Labs. The organization has more than 50 engineers, works with 3+ frontier AI labs across the world, and spans more than 5 workstreams.

- **50+** — engineers in org
- **3+** — frontier AI labs
- **>5** — workstreams

## Skills

- **Generative AI:** Gemini, OpenAI, Anthropic, Computer-use agents, RL (PPO/GRPO), LangChain, Deepgram (ASR), ElevenLabs (TTS)
- **Languages:** TypeScript, Python, JavaScript, Ruby, C++, SQL
- **Frameworks:** Next.js, React, Node.js, FastAPI, Ruby on Rails, GraphQL
- **Libraries:** Redux / RTK Query, React Flow, Prisma, Sentry, Turborepo
- **Infrastructure:** AWS, ECS / EC2 / RDS, OpenSearch / Elasticsearch, MSK · Kafka, Docker, Jenkins, Redis
- **Engineering practices:** Event-driven systems, Role-based access control, A/B + feature flags, Workflow engines

## Contact

I’m happy to talk about AI infrastructure, agent evaluation, and software engineering. Email me or connect on LinkedIn.

- LinkedIn: https://linkedin.com/in/suryateja222
- GitHub (personal): https://github.com/surya-teja-222
- GitHub (work): https://github.com/suryateja-7
- LeetCode: https://leetcode.com/suryateja222
- Résumé: https://itssurya.com/resume.pdf