Principal Full-Stack Software Engineer - Cdmx

About QuadSci.ai: QuadSci.ai is a leading analytics services partner for B2B product and service companies balancing profitable growth & customer experience that today's market demands.
We started QuadSci to answer 2 fundamental questions: Customers demand great experiences and require you to lead them in their product journey…Why isn't this easy in the age of AI? The Field wants to deliver great experiences but internal processes distract from customers…How can AI & Automation create a performance flywheel for the top & bottom line? QuadSci combines telemetry & B2B application data from product analytics, CRMs, support systems, webpages, knowledge bases, and more to fuel our AI products.
Our prescriptive AI insights are deployed via our Intelligent Automation (IA) Libraries which deliver contextualized product experiences, lead your customers in their product adoption journey, automate repetitive processes for your field and provide recommended actions to maximize the financial performance across the top and bottom line for our customers.
QuadSci is partnering with Go-To-Market leaders with a remit to deliver profitable growth via higher resource productivity, improved unit economics, and tech-powered automation to add points to the "Rule of 40" only orchestrating intelligence can provide. About the role We are looking for a Full Stack Engineer to produce scalable software solutions.
We are searching for self-motivated candidates to join a cross-functional team that's responsible for the full software development life cycle, from conception to deployment. As a Full Stack Developer, you should be comfortable around both front-end and back-end coding languages, development frameworks, and third-party libraries.
You'll work across the full stack, building highly scalable, distributed solutions that enable positive user experiences and measurable business growth. We are especially seeking engineers with deep expertise in Elasticsearch, who can architect and optimize search and analytics infrastructure to support high-performance applications and intelligent data retrieval at scale (including enabling RAG for product-aware chat experiences). Responsibilities: Own all aspects of agile software development, including design, implementation, testing, and deploymentArchitect and provide guidance on building end-to-end systems optimized for speed, scale, reliability—and especially search performanceDesign, build, and optimize Elasticsearch indices and clusters, including shard allocation, mappings, and ingestion pipelinesBuild the front-end of applications through appealing visual design and responsive interactionDesign and optimize Elasticsearch clusters and indices, including mapping strategies, shard management, ingestion pipelines, and query performance tuningDevelop infrastructure and workflows for embedding generation using state-of-the-art models (e.g.
OpenAI, Sentence Transformers, Cohere, HuggingFace models)Configure, deploy, and maintain VectorDB solutions such as Elasticsearch (kNN plugin), Qdrant, or FAISS, including index creation, schema design, memory configuration, and performance tuningImplement semantic search pipelines, combining traditional full-text search with vector similarity techniques for hybrid retrievalWrite and optimize APIs that interact with both keyword and vector search backendsCollaborate closely with data scientists and ML engineers on integrating embeddings and vector stores into intelligent featuresBuild responsive front-end components that deliver intuitive user experiences powered by semantic search and rankingEnsure robust observability, logging, and security across search-related infrastructureDocument system architecture, search strategies, data flows, and tuning procedures Basic Qualifications - Must have: At least 5 years of experience building large-scale web software applicationsBachelor's degree (or equivalent) in Computer Science, Information Technology, or EngineeringDeep experience with Elasticsearch architecture, including cluster sizing, tuning, snapshot/restore, scaling strategies, and multi-tenant designExperience designing and integrating RESTful APIsStrong skills in Elasticsearch query ESQL, aggregations, filters, relevance tuning, autocomplete/suggest, and observability (e.g.
slow logs, query profiling)Familiarity with tools in the Elastic ecosystem (e.g.
Kibana, Beats, Logstash, Elastic APM, ECK)Knowledge of front-end languages and libraries (e.g.
HTML/CSS, Vue.js, React, Angular)Knowledge of back-end languages (e.g.
Python, Java, C#) and Node.jsFamiliarity with traditional and NoSQL databases (e.g.
PostgreSQL, MongoDB) and modern web servers (e.g.
NGINX, Apache)Strong communication and collaboration skillsExcellent attention to detail, analytical thinking, and problem-solving skills Advanced Qualifications - Nice to have: Master's degree in Computer Science, Software Engineering, or a related fieldExperience with data ingestion pipelines for Elasticsearch (e.g.
Filebeat, Logstash, custom ETL frameworks)Experience building or integrating RAG systems or document-aware chatbotsFamiliarity with designing and integrating GenAI developer APIs (e.g.
OpenAI, Claude 3)App deployment experience using Docker, Kubernetes, and orchestration on GKE or EKSCloud-native certifications such as AWS Solutions Architect, Google Cloud Developer, or similarFamiliarity with monitoring, observability, and instrumentation tools such as Elastic APM, OpenTelemetry, Prometheus, or GrafanaExperience integrating Elasticsearch with third-party systems like Salesforce, HubSpot, ServiceNow, Zendesk, NetSuite, or PSA platforms