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Principal Engineer
Principal Engineer

Principal Engineer at Recruiting Bond Ā· Bengaluru (Bangalore), Mumbai Ā· 10 - 16 years Ā· ₹75L - ₹130L / yr Ā· Bootstrapped Ā· Posted 22 May 2026

Recruiting Bond's logo

Principal Engineer

Pavan Kumar's profile picture
Posted by Pavan Kumar
10 - 16 yrs
₹75L - ₹130L / yr
Bengaluru (Bangalore), Mumbai
Skills
Distributed Systems
Microservices
Enterprise architecture
System Design & Architecture
Event-Driven Architecture
High-Scale Backend Systems
Scalable Systems Design
API Design (REST, gRPC)
Domain-Driven Design (DDD)
skill iconAmazon Web Services (AWS)
Google Cloud Platform (GCP)
AWS / GCP / Azure
skill iconKubernetes
Kubernetes (K8s)
skill iconDocker
Docker & Containerization
Infrastructure as Code (Terraform)
CI/CD Pipelines
Terraform
Observability (Prometheus, Grafana)
skill icongrafana
prometheus
Data Platforms / Data Engineering
Real-Time Data Processing (Kafka, Flink)
Apache Kafka
AI/ML Systems Architecture
Artificial Intelligence (AI)
skill iconMachine Learning (ML)
LLM Integrations / GenAI Systems
Model Serving / Inference Systems
Technical Strategy
Architecture Leadership
Cross-functional Collaboration
Mentorship & Technical Leadership

🚨 We’re Building a ā€œTop 1% Engineering Orgā€


We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.


About the Role / Team

We are building a next-generation AI-first R&D organization in Bengaluru, focused on solving complex problems across LLMs, Agentic AI systems, distributed computing, and enterprise-scale architectures.


This initiative is part of a publicly listed global company investing heavily in AI-driven transformation, re-architecting its platforms into intelligent, autonomous systems powered by large language models, workflows, and decision engines.


You will be working on:

  • Agentic AI systems & LLM-powered workflows
  • Distributed, scalable backend systems
  • Enterprise-grade AI platforms
  • Automation-first engineering environments


šŸš€ The Mandate

Own and evolve the technical backbone of an AI-first enterprise platform.


You will define architecture across LLM-powered systems, distributed services, and data platforms — and lead critical transformations from legacy → AI-native systems.


🧩 What You’ll Do

  • Architect large-scale distributed systems powering AI-driven workflows
  • Lead 0→1 and 1→N platform builds (LLM integrations, agentic systems, orchestration layers)
  • Redesign legacy systems into scalable, modular, AI-native architectures
  • Drive system design excellence across teams (APIs, infra, observability, reliability)
  • Make high-stakes decisions on trade-offs (latency, cost, scalability, model performance)
  • Mentor senior engineers and influence engineering culture/org standards
  • Partner with product, data, and leadership on long-term technical strategy


🧠 What We’re Looking For

  • Proven track record building high-scale backend or platform systems
  • Deep expertise in distributed systems, microservices, cloud (AWS/GCP/Azure)
  • Strong exposure to data systems/infra / Data / real-time architectures
  • Experience or strong interest in LLMs, GenAI, or AI system design
  • Exceptional system design, abstraction, and problem-solving ability
  • High ownership mindset — you think in terms of systems, not tickets
  • Strong coding skills in Python / Java / Go / Node.js
  • Solid understanding of data structures, system design basics, and backend architecture
  • Experience building scalable APIs and services
  • Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
  • Strong debugging, problem-solving, and ownership mindset
  • Solve hard system problems (latency, scale, reliability)
  • Drive cross-team technical decisions and standards
  • Mentor senior engineers and influence org-wide architectureĀ 
  • Design large-scale distributed systems and backend platforms
  • Mentorship & Technical LeadershipĀ 
  • Expertise in system design, scalability, and performance optimization


Nice to Have

  • Experience integrating LLMs, vector databases, or AI pipelines
  • Contributions to architecture at scale
  • Experience with Agentic AI / LLM orchestration frameworks
  • Background in product engineering or platform companies
  • Exposure to global-scale systems (millions of users / high throughput)


šŸ”„ What Sets You Apart

  • Built platforms used by millions of users / high-throughput systems
  • Experience with event-driven systems, stream processing, or infra platforms
  • Prior work on AI/ML platforms, model serving, or intelligent systems
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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About Recruiting Bond

Founded :
2024
Type :
Services
Size
Stage :
Bootstrapped

About

Recruiting Bond is a global leader in Recruitment Process Outsourcing (RPO), Executive Search, Headhunting, Talent Mapping, and Workforce Consulting. Founded by Pavan B, we are on a mission to power businesses through transformative talent strategies that scale teams, accelerate innovation, and unlock human potential.


With a presence across 25+ industries—from IT, Healthcare, and FinTech to Gaming, BioTech, and Web3—we specialize in hiring that drives outcomes. Our domain expertise spans high-growth startups to Fortune 500 companies, delivering elite CXO and leadership talent, strategic workforce solutions, and inclusive hiring at scale.


We help businesses:

āœ”ļø Hire the right leaders and builders

āœ”ļø Scale globally with speed and precision

āœ”ļø Build talent-first roadmaps from MVP to IPO


Whether you're launching, scaling, or transforming—Recruiting Bond is your strategic partner in talent.


šŸ”¹ Industries: Technology | Healthcare | FinTech | Retail | Manufacturing | EdTech | Crypto | Real Estate | Web3 | Logistics | Energy & more

šŸ”¹ Services: Executive Hiring | RPO | Talent Strategy | Workforce Design | Startup Consulting | Diversity Recruitment


šŸ“Ø Let’s build the future—together: https://recruitingbond.c

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Tech stack

HR Tech

Candid answers by the company

What does the company do?
What is the location preference of jobs?

We help businesses:

āœ”ļø Hire the right leaders and builders

āœ”ļø Scale globally with speed and precision

āœ”ļø Build talent-first roadmaps from MVP to IPO


Whether you're launching, scaling, or transforming—Recruiting Bond is your strategic partner in talent.

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Design and implement multi-tenant data isolation, RBAC, audit logging, and resource quotas appropriate to enterprise-grade products.


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Run code reviews, define conventions, mentor mid-level engineers, and raise the engineering bar.


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Defend architectural decisions, communicate trade-offs, and manage scope.


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Hands-on Production Experience as a Senior or Lead Backend Engineer in Node.js (Mandatory)

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Full stack engineers whose deepest work is on the backend qualify.

POCs and internal tools alone do not qualify.


5+ Years of Professional Backend Engineering Experience

With at least 1 to 2 years in a senior or lead role with direct responsibility for technical decisions and team output.


Deep Node.js and TypeScript Proficiency

Strong with Express, NestJS, or Fastify.

Comfort with async patterns, streams, worker threads, and performance profiling.


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Key Responsibilities

ā— Lead the architecture, design, development, and delivery of scalable backend services

using Python.

ā— Own technical direction and architectural decisions for backend systems and services.

ā— Design and build robust RESTful APIs, microservices, and distributed systems

capable of handling high-scale workloads.

ā— Design and implement real-time communication systems, including WebSocket-based

services, where required.

ā— Drive system performance, scalability, reliability, availability, and security.

ā— Lead performance optimization, capacity planning, monitoring, debugging, and

production issue resolution.

ā— Establish and promote best practices around code quality, testing, observability, CI/CD,

and production readiness.

ā— Collaborate closely with frontend, product, DevOps, infrastructure, and other

cross-functional teams to deliver end-to-end solutions.

ā— Mentor and provide technical guidance to engineers, contributing to overall team growth

and engineering excellence.

ā— Review code and architecture designs, identify technical risks, and drive improvements

across backend systems.

ā— Take ownership of complex technical problems and deliver reliable, maintainable,

production-grade solutions.


Required Skills & Qualifications

ā— 5+ years of software engineering experience, with significant experience in backend

development.

ā— Strong expertise in Python and experience building production-grade backend

applications.

ā— Proven experience designing and developing REST APIs and microservices

architectures.

ā— Strong understanding of distributed systems, scalability, fault tolerance, and

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ā— Hands-on experience with WebSockets and real-time backend integrations.

ā— Strong experience with SQL/NoSQL databases, caching systems, and data-intensive

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ā— Experience with performance optimization, monitoring, logging, debugging, and

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ā— Strong understanding of software engineering principles, design patterns, and clean

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ā— Proven ability to lead technical initiatives and make sound architectural decisions.

ā— Strong communication, collaboration, and technical leadership skills.

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  • Generating client proposals using historical SharePoint data and CRM insights
  • Summarizing meeting transcripts
  • Drafting follow-up communications
  • Feeding structured insights into dashboards and workflow tools

The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.

Key Responsibilities

  • Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
  • Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
  • Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
  • Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
  • Drive architecture decisions balancing scalability, performance, and security
  • Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
  • Mentor junior engineers and evolve into a broader leadership role as the team grows

Ideal Candidate Profile

Experience Requirements

  • 5+ years in full-stack development (Python backend + React/JavaScript frontend)
  • Strong experience in API and microservice integration
  • 2+ years leading technical teams and coordinating distributed engineering efforts
  • 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
  • Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions

Technical Expertise

  • Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
  • Ensuring backend and AI systems are scalable, reliable, observable, and secure
  • Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
  • Experience building production-grade AI systems within enterprise SaaS ecosystems




Read more
Remote only
7 - 12 yrs
₹40L - ₹70L / yr (ESOP available)
Agentic AI
skill iconPython
API management
Anthropic Claude

Experience: 8+ years, senior candidates only | Type: Full-time | Location: Remote (India)


Ā ---

Ā WHAT WE'RE BUILDING


Ā See http://www.juliet.space


Ā We're building Juliet, an AI that runs marketing end to end. Our users are marketers, founders, CEOs, growth leads, agencies, and SMBs — not developers. They

Ā tell Juliet the goal. She plans, writes production code, and ships real marketing: conversion-optimized websites, launch assets, campaigns, audits, autonomously.


Ā That's the engineering problem in one line: the humans in the loop can't read code, so the agent has to get it right on her own — plan, build, self-correct,

Ā recover, ship.


Ā Under the hood: a browser-based studio backed by cloud sandboxes, a real-time SSE streaming pipeline, and a LangGraph agent working across 83 tools and 63 skill

Ā modules. The agent isn't bolted onto the product. She is the product.


Ā Small team, big ambitions. You'll ship things users touch daily, not write tickets about them.


Ā ---

Ā THE ROLE


Ā We're hiring one architect-level backend engineer to own Juliet's agentic infrastructure end to end. That means the agent graph, the execution environment, the

Ā streaming pipeline, the state and memory systems — and setting technical direction for the engineers working alongside you.


Ā This is a player-coach seat. You'll still write code every day, and your architectural calls become the product. You'll work directly with the founder. No PMs in

Ā between.


Ā Frontend is part of the system. You won't be leading it, but you'll need to understand how the agent's output reaches the browser and be able to ship full-stack

Ā features when needed.


Ā ---

Ā THE STACK


Ā AI agent (primary): Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers


Ā API (primary): NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events


Ā Infra (primary): Modal cloud sandboxes, Docker, Netlify deployments


Ā Frontend (secondary): Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js


Ā Monorepo: Turborepo, pnpm


Ā ---

Ā WHAT YOU'LL WORK ON


Ā The majority of your time is here:


Ā Agentic AI workflows — Design, extend, and harden the LangGraph agent graph: multi-step planning, code generation, tool dispatch, self-correction, and recovery

Ā across 83 tools and 63 skill modules. This is the core of the product.


Ā Real-time streaming architecture — The SSE pipeline that carries every agent action from the Python backend through NestJS to the browser: event framing,

Ā reconnection, health monitoring, interrupt handling for plan approvals and clarifying questions.


Ā Agent execution environments — Sandbox lifecycle on Modal: container spin-up, file sync, terminal I/O, command execution, and live preview with per-asset esbuild

Ā bundling. The agent lives here.


Ā State and memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), conversation summarization. How

Ā the agent knows what it knows.


Ā Backend API and data layer — NestJS services, Supabase schema, Redis caching, quota enforcement, webhook handling. The plumbing the agent depends on.


Ā Marketing intelligence pipelines — AEO, CRO, and brand-perception audit engines: multi-LLM probing, parallel inference, streamed structured reports, result

Ā caching. Audit-at-scale infrastructure.


Ā The remaining ~25% of your time:


Ā Full-stack product features — Collaboration (roles and permissions), the Netlify deployment pipeline, subscription and quota flows, onboarding. You'll ship these

Ā end to end — backend first, frontend to close the loop.


Ā ---

Ā WHAT WE'RE LOOKING FOR


Ā Must-have:


Ā - 8+ years of professional software engineering, including meaningful time as a tech lead or systems architect who owned something end to end. Closer to ten is

Ā the norm for people who thrive here.

Ā - Both worlds on your resume: engineering rigor inside a large company and 0-to-1 ownership at an early-stage startup.

Ā - Production agentic systems experience. You've built and operated LLM agent systems in production with LangGraph, LangChain, or equivalent — agent graphs, tool

Ā use, state management, prompt engineering, evals. This means well beyond calling a chat endpoint.

Ā - Strong Python. You design and ship production Python daily. The agent codebase is yours to own.

Ā - Architect-level system design. You can own how data flows across four services, make tradeoffs under uncertainty, and defend every call.

Ā - AI-native development workflow. You drive Claude Code, Codex, or similar agentic tools as everyday instruments — not occasionally. You have opinions about

Ā working with coding agents because you do it constantly.

Ā - Real-time backend systems. You've built SSE, WebSocket, or streaming API infrastructure in production — not just consumed it.

Ā - Strong TypeScript. The API layer and most product features are in TypeScript. You're productive in it.


Ā Strong plus:


Ā - Background in developer tools, IDEs, or coding/execution platforms

Ā - Container runtimes and sandboxed execution (Modal, E2B, Firecracker, or similar)

Ā - Depth in PostgreSQL, Redis, and Supabase

Ā - LLM observability and evals tooling (LangSmith or similar)

Ā - NestJS or equivalent Node.js API framework experience

Ā - React/Next.js — enough to ship a full-stack feature without handoff

Ā - Exposure to marketing, growth, or publisher-facing products


Ā ---

Ā WHY THIS ROLE IS DIFFERENT

Ā Ā 

Ā You own the architecture. Not a feature factory. Not someone else's design doc. The technical execution of an AI product is yours to lead.


Ā The agent is the product. You're not adding AI to an existing system. You're building and operating the system that is the AI. Every architectural decision

Ā touches what Juliet can and can't do.


Ā Hard problems, always. The system spans cloud sandboxes, streaming infrastructure, multi-step agent graphs, and a full-stack web product — for non-technical

Ā users who can't course-correct a broken output. The bar is high.


Ā Small team, real leverage. Your code ships to users the same week. No layers of approval.


Ā ---

Ā HOW TO APPLY


Ā Send us:


Ā 1. A short note on the most complex agentic system you've shipped: what broke, and what you'd redo.Ā A link to something you've built that involves agent graphs, tool use, or autonomous multi-step execution


Ā 2.Ā What is one thing you would improve about Juliet? It could be a feature or a bug.

Read more
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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