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 + 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; 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 Skills Must have: 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: 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

📌 Senior AI Developer (Automators) (México)
🏢 Luxoft
📍 México