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
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
