Ingeniero de Inteligencia Artificial

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 ideal 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 + 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

Qualifications :

• Education details

Required Skills :

• 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

Preferred Skills :

• 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