Founding Engineer - AI Infra & Scale

About Us :

Our client is a tight-knit, ambitious team of IIT Kanpur grads building a signal-first GTM automation platform that makes SDRs and growth teams 10x more effective.

They've spent years at places like JP Morgan, HSBC, Nanonets, and CityMall, and now they are building the next big thing from the ground up - with real customers, real data, and real scale.

They have grown 13x in the last year and is one of the fastest-growing startups out of India building for the world.

For engineers, that means :

- Working on high-volume data pipelines, LLM-heavy workflows, and workflow orchestration - not toy apps.

- Building systems that sit at the intersection of AI, growth, and SaaS, where performance and reliability actually move revenue.

- A chance to shape the technical and product DNA of a company that's growing fast.

If you've ever wanted to own the systems that keep a serious AI + SaaS product fast and rock-solid as it scales, this is that window.

The Role :

We're hiring a Founding Engineer focused on Scale, Stability and AI infra.

As we ramp from thousands to millions of AI and enrichment calls daily, latency, throughput, and reliability become first-class product features. This role owns that frontier - making sure we stay fast, predictable, and unbreakable as load grows 10x and then 10x again.

You'll go deep into our Go backend, our AI/LLM infra, our data layer, and the other performance edges - wherever scale hurts the most.

What are we looking for?

- 2-3+ years of experience building production backend systems that have actually been stressed by real traffic.

- Someone who treats p99 latency, throughput, and error rates as product metrics - not afterthoughts.

- Deep ownership mindset : you don't just file a fix, you make sure it never happens again.

- Comfortable in a high-intensity, highly agile environment.

- Pragmatic engineer : knows when to ship scrappy and when to go deep on architecture.

- Hungry to learn new languages, tools, and stacks on their own.

What will you do?

- Own performance and reliability of our Go backend services - from API latency to background workers to long-running enrichment pipelines.

- Design and build rate limiting, queueing, and backpressure systems that keep the platform predictable under spiky, bursty load.

- Scale our NoSQL and search layer - schema design, query optimization, sharding, hot-key mitigation, and Elasticsearch / OpenSearch tuning.

- Optimize our AI/LLM infrastructure - gateways, token routing, batching, caching, and fallbacks across model providers - so we ship faster responses at lower cost.

- Hunt down latency on both frontend and backend perf edges where it matters - initial paint, hydration, data fetching patterns, real-time updates.

- Build the observability layer - metrics, traces, alerts, runbooks - so we catch regressions before customers do.

- Lead load testing, capacity planning, and incident response as we step up to each new order of magnitude.

- Directly influence architecture and roadmap with the founders.

You won't be "just another dev" - you'll be the person who makes sure at 10x scale feels as fast as at 1x.

Responsibilities :

- Go (Golang) :

1. Hands-on, fluent. You can read, write, and refactor production Go services.

2. Importance : Must have

- Production scale exp

1. Has run something serving 10k+ QPS or comparable real-world load.

2. Importance : Must have

- Rate limiting & queueing :

1. Has built (not just used) rate limiters, work queues, or backpressure systems.

2. Importance : Must have

- AI / LLM infra

1. Has shipped LLM-backed systems in production - model gateways, batching, caching, cost & latency tuning.

2. Importance : Must have

- NoSQL databases

1. Hands-on with MongoDB / DynamoDB / Cassandra / similar - schema, query patterns, sharding.

2. Importance : Must have

- Elasticsearch / OpenSearch

1. Has tuned indexes, queries, and clusters at non-trivial scale.

2. Importance : Should have

- Distributed systems fundamentals

1. Comfortable reasoning about consistency, idempotency, retries, and failure modes.

2. Importance : Must have

- Observability :

1. Comfortable with metrics, traces, profiling, and turning data into action.

2. Importance : Must have

- Next.js / React :

1. Comfort with frontend perf work - bundle size, hydration, data fetching.

2. Importance : Should have

- AWS / GCP :

1. Hands-on with cloud primitives - compute, networking, managed datastores.

2. Importance : Should have

- Workflow engines, event-driven systems :

1. Built or operated something like Kafka, Temporal, SQS, Redis Streams.

2. Importance : Nice to have

Pre-requisite Tech Stack :

- Go, NoSQL (MongoDB / DynamoDB / similar), Redis, Elasticsearch / OpenSearch, AWS

Good to Have :

- Previous start-up or early-stage experience.

- Deep interest in system design, performance engineering, and scaling.

- Experience with event-driven systems, queues, or workflow engines.

- Comfort going below the abstraction - profilers, flame graphs, EXPLAIN plans, network traces.

Success in your first 6 months :

- p99 latency cut by 50%+ on the hottest paths in the product.

- Platform handles 10x current load without rate-limit-driven incidents or outages.

- LLM cost and latency measurably down - through smarter routing, batching, and caching.

- A clear, instrumented view of where the next bottleneck is - before it bites.

Location :

- Gurgaon (in-office)

We're building fast and tight - this is a high-context, high-collaboration environment.

What's in it for you? :

- Work directly with the founders on product, tech, and architecture

- Join right as we go from 01 to 1100 - not theory, real scale

- Own the systems that keep an AI + SaaS product fast and reliable at 10x growth

- Massive ownership over architecture, infra, and direction

- Competitive compensation + meaningful ESOPs - real upside, not just salary

- See your work used by real GTM teams at fast-growing B2B companies

Must-haves :

- Go (Golang) - fluent, production-level

- 23+ years building systems at real scale (10k+ QPS)

- Rate limiting, queuing & backpressure systems (built, not just used)

- AI/LLM infra - model gateways, batching, caching, cost & latency tuning

- NoSQL - MongoDB / DynamoDB / similar

- Distributed systems fundamentals

- Observability - metrics, traces, profiling

Good to have :

- Elasticsearch, Next.js/React, AWS/GCP, Kafka/Temporal/Redis Streams