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:

- o 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
- o AI-based Change-Based Testing (CBT) — LLM-driven test case selection using semantic similarity between code changes and test coverage
- o 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; 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 team

- • Present AI solution demos to stakeholders and engineering leadership

- • Document AI system architecture, RAG pipelines, and tools in Confluence

Mandatory Skills Description:

- • 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-turn

- • Vector 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 LlamaIndex

- • 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 / UiAutomator2 — mobile/Android UI automation

- • Android / 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, OpenSearch Serverless

- • Docker — containerized test execution environments

Nice-to-Have Skills Description:

- - Cursor IDE advanced features — .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

- - Streamlit — for building internal AI dashboards

- - DSPy — programmatic prompt optimization

- - AWS SageMaker / MLflow — model evaluation and experiment tracking

- - Kotlin — for tooling alongside Java

📌 Artificial Intelligence Engineer (México)
🏢 Luxoft
📍 México