Artificial Intelligence Engineer

Who We Are Looking For

1. Deep Backend Core & Infrastructure Chops (8 Years Experience)

They are, first and foremost, a hardcore software engineer. Crucially, we are not looking for a

pure Machine Learning Engineer or Data Scientist who spends their time training models or

working exclusively in Jupyter notebooks. They must have spent years building, scaling, and

maintaining complex backend systems. They know what architectural decisions cause

bottlenecks at scale, how to design robust APIs, and how to build systems that don't break

under heavy load.

2. The Pure Builder (100% IC)

They are a dedicated, high-impact Individual Contributor. Their leverage comes from their

technical architecture, the scalability of their systems, and their raw code output. They lead by

technical example and architectural vision.

3. The AI-Augmented Developer

They don't just build for AI; they build with AI. They are fluent in the new paradigm of software

engineering, aggressively leveraging tools like Claude Code, GitHub Copilot, and Cursor to

multiply their output. They can build end-to-end applications from scratch at a velocity that

wasn't possible two years ago.

4. Plugged into the AI Ecosystem

They possess a deep, practical understanding of the modern AI software stack. They go beyond

surface-level API calls and understand vector databases, LLM orchestration frameworks,

retrieval-augmented generation (RAG) pipelines, and model serving infrastructure. They know

how to integrate AI components into a broader, scalable software architecture.

5. High Agency & Startup Hustle

They operate with extreme resourcefulness. In a high-ambiguity environment, they do not wait

for perfectly scoped requirements or external blockers to clear. They have a growth mindset,

figure out the path forward, and ship relentlessly.

How to Find Them

This is a highly competitive profile. We are looking for a rare intersection of rigorous traditional

backend scaling experience and cutting-edge AI fluency.

Where to Source (Target Pools):

● AI-Native Startups: Engineers currently at Series A-C startups building foundational AI

tools, developer tools, or AI-first enterprise SaaS.

● Platform/Core Teams at Tech Tier 1/2: Look for teams labeled Core Infrastructure,

Backend Platform, or Applied AI Platforms at companies known for heavy engineering

cultures (e.g., Swiggy, Razorpay, Zepto, Flipkart, or the India core-engineering hubs of

global tech majors).

Keywords & Resume Signals:

● Backend/Scale (Primary Signal): Distributed Systems, Microservices, Go, Rust,

Java/Kotlin, Kubernetes, Kafka, gRPC, High-throughput, Low-latency.

● AI/ML Integration Stack: Vector Databases (Pinecone, Weaviate, Milvus), RAG,

LLMOps, Model Serving / Inference (Triton, vLLM).

● Action Verbs: Look for Architected, Built from scratch, Designed the platform,

Scaled backend from X to Y.

Green Flags to Look For in Screening:

● Side Projects/Hacking: They actively build their own AI products or integrations on

nights and weekends just to test new models or frameworks.

● Tooling Obsession: When asked about their workflow, they enthusiastically detail how

they use Claude, Cursor, or Copilot to automate boilerplate and write tests.

● Pragmatism: They can articulate when not to use an LLM or complex AI solution,

showing they value engineering pragmatism over hype.

Anti-Patterns (Red Flags for this specific role):

● The Pure ML Modeler: Candidates whose experience is heavily dominated by model

training, feature engineering, data science, and hyperparameter tuning, but lack a proven

track record of building and deploying high-throughput backend services and APIs.

● The Prompt Engineer: Profiles that are heavy on prompt tuning and building basic UI

wrappers/chatbots, but lack the 8+ years of deep backend/database/infrastructure scaling

experience.

● The Architect-Only: Candidates who draw diagrams but haven't pushed production code

themselves in the last 12-18 months. We need someone who is hands-on.

● Over-indexing on Process: Candidates who heavily emphasize Agile ceremonies, Jira

management, and team sizes over technical outcomes and architecture.