About the Company : We are building and maintaining one of the largest OTT platform test automation frameworks, serving millions of customers across streaming TV platforms. The team develops a Java/Appium-based automation framework for Android TV devices and is actively expanding it with AI-powered tooling.

About the Role : We are looking for a Senior AI Developer. This is a hybrid role combining the design and development of AI-powered internal tools with hands-on test automation engineering skills. The idóneo candidate is a software engineer who understands both QA automation and modern LLM/RAG systems — and can translate test engineering problems into practical AI solutions.

Automated test failure triage — LLM + RAG pipeline classifying ReportPortal failures (logs, stack traces, screenshots) into structured categories (PRODUCT_BUG, AUTOMATION_BUG, SYSTEM_ISSUE) using AWS Bedrock + Claude
AI-based Change-Based Testing (CBT) — LLM-driven test case selection using semantic similarity between code changes and test coverage
AI test case generation from feature specs, Jira tickets, and Confluence documentation
Build and maintain end-to-end RAG pipelines: document ingestion → chunking → embedding → OpenSearch Serverless vector store → retrieval → LLM response generation
Develop AWS Lambda functions (Python 3.12) and API Gateway REST endpoints to integrate AI capabilities into CI/CD pipelines
Apply prompt engineering best practices (system prompts, structured JSON output, guardrails) and drive continuous evaluation of LLM solution accuracy
Use Cursor IDE with MCP integrations, agentic workflows,



and context/rules files to accelerate test code generation and maintenance
Write, maintain, and expand automated test suites in Java (Appium / UiAutomator2) for Android TV platforms
Develop and maintain functional, regression, NFR, and CBT test suites
Triage and resolve test failures in ReportPortal; Support CI/CD pipeline health — participate in Nightly Build, RC, and release automation runs via Jenkins
Participate in Kanban ceremonies and PI planning under the ART team
Document AI system architecture, RAG pipelines, and tools in Confluence

AWS Bedrock — hands-on: model access, Knowledge Bases, Lambda integration (primary AI platform)
Prompt engineering — zero-shot, few-shot, chain-of-thought, structured output (JSON mode), multi-turn
Vector databases — working knowledge of OpenSearch, Pinecone, or Faiss; Python — for Lambda functions, AI pipeline scripting, and data processing
Java — 3+ years of hands-on test automation development
Appium / UiAutomator2 — mobile/Android UI automation
Android / ADB — device management, test execution
ReportPortal or equivalent test reporting tool
Jenkins / CI-CD — pipeline debugging and integration
AWS — S3, Lambda, API Gateway, IAM, OpenSearch Serverless
Docker — containerized test execution environments

cursorrules, memory-bank context files, MCP server integration, and agentic triage workflows
Android TV platforms — STB / embedded device testing experience (Fire TV, Roku, or similar)
QMetry (QTM4J) — test management integrated with Jira
AWS SageMaker / MLflow — model evaluation and experiment tracking
Kotlin — for tooling alongside Java

📌 Ingeniero Inteligencia Artificial (Ciudad de México)
🏢 Luxoft Mexico
📍 Ciudad de México