Principal Architect

Netcore Unbxd; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Netcore Unbxd is seeking a Principal Architect to own the technical direction of the Search, Recommendations, and AI Engineering Platform for a high-throughput multi-tenant SaaS. The role is a senior IC, requiring hands-on coding and deep architectural leadership across distributed systems.

The candidate will set strategy for vector search, embeddings, and agentic AI integration with traditional search stacks while collaborating with Merchandising Console and other surfaces to maintain

Qualifications

• 8+ years architecting and operating large-scale distributed systems in production.
• Hands-on coding; IC role with deep technical depth.
• Experience with information retrieval systems at scale (Solr/Elasticsearch/Lucene).
• Experience integrating vector search/embedding retrieval with traditional search.
• Track record in multi-tenant SaaS with strict SLA commitments.

Responsibilities

• Own and evolve architecture of Search & Recommendations platform for enterprise retail.
• Define multi-year technical strategy for AI engineering platform and how it integrates with core retrieval.
• Make build-vs-buy and cost-vs-performance tradeoffs at platform level.
• Provide technical advisory to adjacent teams ensuring architectural coherence.
• Hands-on coding, proofs of concept, and deep debugging for systemic issues.
• Collaborate with leadership to translate business requirements into architecture.
• Establish technical standards and serve as escalation point for reliability challenges.
• Represent platform in customer/partner technical discussions requiring credibility.
• Track emerging tech (search, vector databases, agentic AI) for roadmap input.

Skills

Java

Go

Python

Information retrieval

Distributed systems

Vector search

Embeddings

LLM-based AI

Tools

Solr

Elasticsearch

Lucene

Job description

Principal Architect – Search, Recommendations & AI Platform

About Netcore Unbxd

Netcore Unbxd is an AI-powered product discovery platform recognized by Forrester and Gartner as the global leader in Commerce Search and Product Discovery, earning Leader status in Gartner's 2025 Magic Quadrant™ for the second consecutive year. We're trusted by billion-dollar retailers like Unilever, Waitrose, Express, Puma, Signet, and Advance Auto Parts to power conversions and revenue through best-in-class product discovery and personalization. We're at the forefront of Agentic AI in commerce — building AI Shopping Assistants and Autonomous Merchandising Agents that redefine how shoppers find what they want.

Curious what it's like to work here? Check out Life at Netcore

Also Please have a look at our Founder's take on where we're headed: https://www.founderthesis.com/p/rajesh-jain-and-netcore-cloud-the

Role Overview

We're looking for a Principal Architect to own the technical direction of Unbxd's Search, Recommendations, and AI Engineering Platform — the core system processing millions of API calls a day across a multi-tenant SaaS platform, under tight SLAs, for some of the world's largest retailers. This is a senior individual-contributor role, not a people-management position. You'll be the deepest technical authority in the room: setting architecture strategy, making hard distributed-systems tradeoffs, and being hands-on enough to prototype, debug, and review code at the level of detail that actually moves the needle.

You'll also act as a technical advisor across adjacent areas of the platform — including the Merchandising Console and other supporting systems — helping ensure architectural coherence across Unbxd's broader product surface, even though your primary charter is search, recommendations, and the AI engineering platform.

Experience:

12+ years in software engineering, with 8+ years of demonstrable architecture-level ownership of large-scale, high-throughput distributed systems.

Core Skills:

Java, Go, Python; deep expertise in information retrieval systems (Solr/Lucene or equivalent); distributed systems fundamentals (consensus, sharding, replication, caching, queuing); production experience with vector search / embedding-based retrieval and its integration into traditional search stacks.

Key Responsibilities

• Own and evolve the architecture of Unbxd's Search & Recommendations platform — a Solr-based system supporting millions of API calls/day at strict SLA targets for enterprise retail customers.
• Set the multi-year technical strategy for the AI Engineering Platform, including how vector search, embeddings, and LLM-based agentic components (AI Shopping Assistant, Autonomous Merchandising Agents) integrate with the core retrieval stack.
• Make and defend architectural decisions on scalability, fault tolerance, multi-tenancy, and cost-efficiency for a SaaS platform operating at retail scale (including peak-traffic events like flash sales and holiday spikes).
• Act as a technical advisor to adjacent teams (Merchandising Console, self-serve, delivery/CX platforms), ensuring their designs align with overall platform architecture, without owning their day-to-day delivery.
• Stay hands-on — write and review code, build proofs of concept for new architectural directions, and go deep on production issues when systemic root-causing is needed.
• Partner with engineering leadership, product management, and customer-facing teams to translate business and customer requirements (e.g., SLA commitments, billing model design, integration patterns for SIs/agencies) into sound technical architecture.
• Define technical standards, review architecture proposals across teams, and act as an escalation point for the hardest distributed-systems and reliability problems.
• Represent Unbxd's technical platform in customer-facing or partner-facing technical discussions where deep architectural credibility is required.
• Track and evaluate emerging technology (search, vector databases, agentic AI infra) and translate it into pragmatic recommendations for the platform roadmap.

Qualifications

• 8+ years architecting and operating large-scale distributed systems in production, ideally in search, recommendations, or a related high-QPS domain.
• Strong, current coding ability — this is an IC role and hands-on technical depth is non-negotiable, not a legacy credential.
• Proven experience with information retrieval systems (Solr, Elasticsearch, Lucene, or similar) at scale.
• Practical experience integrating vector search / embedding-based retrieval with traditional keyword search systems.
• Track record of operating systems under strict SLA/availability commitments in a multi-tenant SaaS environment.
• Experience influencing architecture across multiple teams/domains without formal reporting authority — strong technical persuasion and cross-functional collaboration skills.
• Comfortable making build-vs-buy and cost-vs-performance tradeoffs at a platform level (e.g., billing model design, infra migration strategy).
• Prior exposure to agentic AI systems or LLM-based production features is a strong plus, given Unbxd's direction into Agentic AI.