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 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 Report Portal 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 → Open Search 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 / Ui Automator2) for Android TV platforms
- Develop and maintain functional, regression, NFR, and CBT test suites




- Triage and resolve test failures in Report Portal; integrate AI triage results with QMetry (QTM4 J)
- 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 ConfluenceQualifications :- Education detailsRequired 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 Open Search, 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 — Lang Chain, Lang Graph, 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 automation
- Android / ADB — device management, test execution
- Report Portal 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, Open Search Serverless
- Docker — containerized test execution environmentsPreferred Skills :

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