Architect - Search Relevance

Myntra · Bengaluru

  • Experience11–15 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelexecutive
  • Posted2 Sept 2026
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About Myntra

Myntra is hiring in Bengaluru in ecommerce retail. This role looks for around 11+ years of experience.

Skills

  • machine learning
  • NLP
  • search relevance
  • query understanding
  • ranking
  • retrieval
  • classification
  • embeddings
  • neural networks
  • Transformers
  • BERT
  • T5
  • large language models
  • RAG
  • Elasticsearch
  • Solr
  • OpenSearch
  • Python
  • Java
  • Scala
  • Go
  • distributed systems
  • data pipelines
  • model serving
  • online feature stores
  • deployment pipelines
  • statistical evaluation
  • A/B testing

The role

A search relevance architect at a fashion and lifestyle e-commerce marketplace designs machine learning, NLP, and generative search systems for query understanding, ranking, retrieval, and personalized discovery. The role builds distributed systems and low-latency model serving for production search experiences, applying Elasticsearch and RAG to improve relevance.

Full job description

We're looking for an Architect (search relevance) to own the end-to-end search relevance stack for a high-scale fashion and lifestyle e-commerce platform serving millions of customers and a catalogue of 20M+ styles. This is a high-impact IC architecture role at the intersection of machine learning, NLP, search, GenAI, and large-scale distributed systems.

The candidate will have responsibilities across the following functions:

Search Intelligence and Relevance:

Design and own query understanding: intent classification, category prediction, attribute extraction, query rewriting, and spell correction.

Build and fine-tune LLM and NLP models for domain-specific query comprehension.

Own autocomplete, personalized typeahead, trending, and session-aware suggestions.

Drive hybrid search combining lexical and dense/semantic retrieval.

Solve low-recall and zero-result queries through query expansion, taxonomy mapping, and semantic fallback.

Establish rigorous evaluation using NDCG, MRR, offline judgment sets, and A/B experimentation.

Rapidly evaluate emerging LLMs, RAG, and generative search techniques and take successful ideas to production.

Systems and Architecture:

Own the technical architecture across retrieval, indexing, model serving, and online inference.

Design model-serving infrastructure, including feature computation, model versioning, A/B routing, shadow deployments, and latency optimization.

Build catalogue enrichment and signal-generation pipelines for retrieval and NLP models.

Drive build-vs-buy decisions and establish engineering standards for production ML systems.

Requirements:

10-15 years of industry experience.

4+ years building production search relevance or recommendation systems at scale.

Strong ML fundamentals across ranking, retrieval, classification, embeddings, and neural networks.

Strong hands-on NLP/Transformer experience with BERT, T5 or similar.

Experience with LLM-based query rewriting, RAG, or generative search.

Conceptual understanding of Elasticsearch, Solr, or OpenSearch.

Strong Python skills with the ability to review production code in Java/Scala or Go.

Experience with distributed systems, data pipelines, and low-latency model serving.

Hands-on exposure to model-serving infrastructure, online feature stores, and deployment pipelines.

Strong experimentation and statistical evaluation mindset.

Good to Have:

Hands-on experience with Elasticsearch/Solr/OpenSearch relevance tuning.

Experience with Qdrant, Milvus, Vespa, FAISS, or other ANN systems.

Knowledge of e-commerce taxonomy or knowledge graphs.

Publications or applied research contributions in IR, NLP, or recommendation systems.