DevSecOps Engineer

Menlo Ventures; Mumbai; On-site; INR 800,000 - 1,500,000; Full time

Job summary

Menlo Ventures is seeking an AI Engineer to join the proAgent pod in Mumbai. In this hands-on role, you will be responsible for building components of the agentic runtime that powers financial conversations, working closely with a talented team.

Your role will involve deep coding work, real-time voice processing, and collaborating on innovative AI systems. You will take ownership of your features and contribute significantly right from day one.

The opportunity offers strong mentorship and the chance to impact millions of conversations in the fintech space.

Qualifications

• 2–5 years of hands-on engineering experience with ML or AI systems.
• Solid Python fundamentals; familiar with TypeScript is a plus.
• Experience with prompt engineering and API integrations.

Responsibilities

• Build and maintain agent runtime components for financial conversations.
• Work on real-time voice pipeline with low latency.
• Write and refine prompts and implement orchestration flows.

Skills

Hands-on engineering experience

Python fundamentals

Experience with LLMs

Fast learning and ownership

Tools

Twilio

LiveKit

ElevenLabs

Deepgram

Job description

About the Role

We're looking for an AI Engineer to join the proAgent pod and work on the systems that power our autonomous voice agents in live financial conversations. You'll work alongside a small, high‑agency team where you will have real ownership over features and components from day one.

This is a hands‑on engineering role. You'll be deep in the code — building agent logic, integrating speech models, tuning prompts, and debugging gnarly real‑time issues. You don't need to have done all of this before, but you need to be the kind of engineer who figures things out fast, takes feedback well, and ships.

What You'll Do

Agent Loop & Reasoning

• Build and maintain components of the agentic runtime that powers multi‑turn financial conversations — covering payment negotiations, compliance guardrails, and objection handling
• Implement context management logic that tracks consumer state, conversation history, and business rules across long, branching dialogues
• Write and iterate on primitives that balance conversational fluidity with structured reasoning — the agent needs to feel human while making verifiable decisions

Voice AI Systems

• Work on our real‑time voice pipeline with a target of sub‑1s latency across transcription, reasoning, and synthesis
• Contribute to VAD tuning and turn‑taking logic that makes conversations feel natural
• Help evaluate and integrate speech models (STT, TTS, speech‑to‑speech) — we currently work with ElevenLabs and Cartesia for TTS, and Deepgram for STT, and are always exploring what's next
• Debug streaming audio and WebRTC issues in production

LLM Infrastructure

• Write and refine prompts, implement orchestration flows, and contribute to model routing logic
• Build components of our evaluation framework — helping measure agent quality across conversation quality, empathy, and compliance adherence
• Stay curious about new models and tools — flag opportunities and contribute to build‑vs‑integrate discussions

What You Bring

• 2 – 5 years of hands‑on engineering experience, with good exposure to building or working with ML or AI systems in production.
• Solid Python fundamentals — you are comfortable writing clean, maintainable code and debugging production issues.
• Some experience working with LLMs: prompt engineering, API integrations, or building simple pipelines or agents.
• High bias for action — you don't wait to be told exactly what to do, and you push yourself to ship rather than over‑engineer.
• Strong fundamentals in Python; familiarity with TypeScript is a plus.
• Eager to learn in a fast‑moving environment, take ownership of your work, and ask good questions.

Even Better

• Exposure to voice AI: speech recognition, synthesis, or telephony systems — even if only through personal projects or coursework
• Any background or interest in fintech, lending, or collections
• You've tinkered with Twilio, LiveKit, ElevenLabs, or similar real‑time infrastructure
• You've built a small agentic system — even a side project — that combines LLM reasoning with structured actions
• Familiarity with streaming protocols, WebRTC, or low‑latency system design

Why This Role

• Real ownership from day one: proAgent is a small pod. You won't be a cog — you'll own components, ship features, and see your work in live consumer conversations within weeks.
• Frontier work: voice AI that reasons, decides, and acts is one of the hardest problems in applied AI. You'll be learning by doing on problems most engineers never touch.
• Strong mentorship: you'll work directly with senior engineers and the AI Lead who will invest in your growth — this is a place to level up fast.
• High leverage early career: the decisions you make and the code you write will impact millions of financial conversations. Rare for an early‑career role.