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