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 adecuado 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

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📌 Senior AI Developer (Ciudad de México)
🏢 Luxoft
📍 Ciudad de México