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 Report Portal failures (logs, stack traces, screenshots) into structured categories (PRODUCT_BUG, AUTOMATION_BUG, SYSTEM_ISSUE) using AWS Bedrock + ClaudeAI-based Change-Based Testing (CBT) — LLM-driven test case selection using semantic similarity between code changes and test coverageAI test case generation from feature specs, Jira tickets, and Confluence documentationBuild and maintain end-to-end RAG pipelines: document ingestion → chunking → embedding → Open Search Serverless vector store → retrieval → LLM response generationDevelop AWS Lambda functions (Python 3.12) and API Gateway REST endpoints to integrate AI capabilities into CI/CD pipelinesApply prompt engineering best practices (system prompts, structured JSON output,



guardrails) and drive continuous evaluation of LLM solution accuracyUse Cursor IDE with MCP integrations, agentic workflows, and context/rules files to accelerate test code generation and maintenanceWrite, maintain, and expand automated test suites in Java (Appium / Ui Automator2) for Android TV platformsDevelop and maintain functional, regression, NFR, and CBT test suitesTriage and resolve test failures in Report Portal; Support CI/CD pipeline health — participate in Nightly Build, RC, and release automation runs via JenkinsParticipate in Kanban ceremonies and PI planning under the ART teamDocument AI system architecture, RAG pipelines, and tools in ConfluenceAWS 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-turnVector databases — working knowledge of Open Search, Pinecone, or Faiss; Python — for Lambda functions, AI pipeline scripting, and data processingJava — 3+ years of hands-on test automation developmentAppium / Ui Automator2 — mobile/Android UI automationAndroid / ADB — device management, test executionReport Portal or equivalent test reporting toolJenkins / CI-CD — pipeline debugging and integrationAWS — S3, Lambda, API Gateway, IAM, Open Search ServerlessDocker — containerized test execution environments

📌 Ingeniero De Inteligencia Artificial - (Xico)
🏢 Luxoft Mexico
📍 Xico