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.Responsibilities:Design and implement AI-powered solutions focused on: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 + 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 → OpenSearch 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 / UiAutomator2) for Android TV platformsDevelop and maintain functional, regression, NFR, and CBT test suitesTriage and resolve test failures in ReportPortal; integrate AI triage results with QMetry (QTM4J)Support CI/CD pipeline health — participate in Nightly Build, RC, and release automation runs via JenkinsContribute to framework codebase improvements — bug fixes, refactoring, enhancementsParticipate in Kanban ceremonies and PI planning under the ART teamPresent AI solution demos to stakeholders and engineering leadershipDocument AI system architecture, RAG pipelines, and tools in ConfluenceQualifications:Education detailsRequired Skills:AWS Bedrock — hands-on: model access, Knowledge Bases, Lambda integration (primary AI platform)AI agents & Agentic tooling — practical knowledge of designing and operating AI agents, including agentic workflows, reusable skills, rules/guardrails, commands, and multi-tool/multi-agent orchestrationRAG pipeline — end-to-end implementation: chunking, embedding, vector indexing, retrieval,



generationPrompt engineering — zero-shot, few-shot, chain-of-thought, structured output (JSON mode), multi-turnVector databases — working knowledge of OpenSearch, Pinecone, or Faiss; understands vector vs. graph DB differenceLLM guardrails — input/output filtering, hallucination mitigation strategiesFine-tuning vs. RAG — ability to reason through which approach fits a given problemLLM orchestration — LangChain, LangGraph, or LlamaIndexEmbeddings — understands semantic similarity; experience with Amazon Titan Embed or equivalentPython — for Lambda functions, AI pipeline scripting, and data processingJava — 3+ years of hands-on test automation developmentAppium / UiAutomator2 — mobile/Android UI automationAndroid / ADB — device management, test executionReportPortal or equivalent test reporting toolREST API — concepts and hands-on usageJenkins / CI-CD — pipeline debugging and integrationAWS — S3, Lambda, API Gateway, IAM, OpenSearch ServerlessDocker — containerized test execution environmentsPreferred Skills:Cursor IDE advanced features — .
cursorrules, memory-bank context files, MCP server integration, and agentic triage workflowsAndroid TV platforms — STB / embedded device testing experience (Fire TV, Roku, or similar)QMetry (QTM4J) — test management integrated with JiraStreamlit — for building internal AI dashboardsDSPy — programmatic prompt optimizationAWS SageMaker / MLflow — model evaluation and experiment trackingKotlin — for tooling alongside Java

📌 Ingeniero De Inteligencia Artificial (México)
🏢 Luxoft Mexico
📍 México