Senior AI Developer (Automators)

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 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: o 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 + Claudeo AI-based Change-Based Testing (CBT) — LLM-driven test case selection using semantic similarity between code changes and test coverageo 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 (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 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 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 Sage Maker / MLflow — model evaluation and experiment tracking
• Kotlin — for tooling alongside Java
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