- 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 orchestration
- RAG pipeline - end-to-end implementation: chunking,embedding, vector indexing, retrieval, generation
- Prompt 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 difference
- LLM guardrails - input/output filtering, hallucination mitigation strategies Fine-tuning vs. RAG - ability to reason through which approach fits a given problem
- LLM orchestration - LangChain, LangGraph, or Llama Index Embeddings - understands semantic similarity; experience with Amazon Titan Embed or equivalent Python - for Lambda functions, AI pipeline scripting, and data processing
- Java - 3+ years of hands-on test automation development Appium / Ui Automator2 - mobile/Android UI automationAndroid / ADB - device management, test execution ReportPortal or equivalent test reporting tool
- REST API - concepts and hands-on usage Jenkins / CI-CD - pipeline debugging and integration AWS - S3, Lambda, API Gateway, IAM, Ope nSearch Server less Docker - containerized test execution environments

Project Description

- 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
- 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 coverage AI test case generation from feature specs, Jira tickets, and Confluence documentation
- 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 platformsDevelop and maintain functional, regression, NFR, and CBT testsuites
- Triage 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 Jenkins
- Contribute to framework codebase improvements - bug fixes, refactoring, enhancements Participate 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 Confluence

#J-18808-Ljbffr

📌 Senior AI Developer (México)
🏢 Luxoft
📍 México