Search Expert Engineer

About the Role This role sits at the heart of our search infrastructure, focusing on data ingestion, embedding generation, indexing, and query performance at scale. You will work closely with platform engineers to ensure our search services are fast, reliable, and ready to support the next generation of AI-driven products. Most Important Expert-level proficiency in Python, with hands-on experience building production-grade Back-End services, data pipelines, or ETL workflows. Expert Python Proficiency: specific experience building production-grade backend services, data pipelines, or ETL processes using Python (e.g., FastAPI, Flask, or complex scripting). Experience in Python libraries (NumPy, NLTK) is required NLTK (Natural Language Toolkit) for working with human language (text) NumPy for numerical and scientific computing in Python Natural Language Processing (NLP) Expertise: Demonstrated experience with fundamental NLP concepts, including text processing, language analysis (e.g., tokenizing, parsing), and common NLP tasks (e.g., classification, named entity recognition). Deep, practical experience with OpenSearch or ElasticSearch, including index design, mappings, analyzers, sharding strategies, and query optimization. Node.js/TypeScript Knowledge: As the surrounding system software is based on this stack, strong abilities in Node.js/TypeScript code is required for cross-team collaboration and integration across services. Proven experience managing search systems at scale, delivering sub-second query latency across tens of millions of documents. Hands-on experience implementing vector search or semantic retrieval using k-NN plugins or vector databases such as Milvus, Qdrant, or Pinecone. Strong understanding of search engine internals, including tokenization, inverted indices, and cluster behavior. Experience designing and running safe, non-disruptive data migrations, re-indexing workflows, and data cleanup processes. Ability to debug and optimize distributed systems, identifying bottlenecks between application logic and search infrastructure.​​​​ Cloud Infrastructure: Experience with AWS (specifically managed OpenSearch Service, Lambda, EC2, ECS, API Gateway, Cloudwatch Logs, Load Balancers (ALB/NLB/GWLB) Nice to Have Experience with Retrieval-Augmented Generation (RAG) pipelines and search relevance evaluation techniques such as recall@K. Familiarity with Large Language Models or frameworks such as LangChain. Experience in other Python libraries (Pandas, Boto3) is advantageous Personal Traits Strong ownership mindset, with the ability to lead and evolve critical infrastructure independently. Detail-oriented and performance-focused, with a passion for building fast, reliable systems. Comfortable collaborating cross-functionally with engineers working in different languages and stacks. Clear communicator who can explain complex technical concepts to both technical and non-technical partners. Curious and adaptable, with an interest in modern semantic search and AI-powered retrieval systems. preferred Location LATAM