Quality Assurance Engineer – AI Agentic Applications, AVP

Overview

Quality Assurance Engineer to validate AI agentic applications across development and production, combining specialized AI testing, automation, safety evaluation and continuous quality measurement.

What you'll do

• Design, execute and maintain test strategies and automation frameworks for AI-powered agentic workflows
• Develop and implement specialized testing approaches for LLM-powered applications, validating response accuracy, relevance and grounding, and identifying hallucinations and edge-case behaviours
• Test and evaluate the behaviour of autonomous and semi-autonomous AI agents, ensuring proper planning, tool selection/sequencing, agent-to-agent interactions and graceful error/timeout recovery
• Validate Retrieval-Augmented Generation (RAG) performance, including document ingestion, chunking, metadata quality, embedding accuracy and citation/source attribution
• Perform AI safety and security testing to identify vulnerabilities such as prompt injection (direct/indirect), data leakage, unauthorized tool execution and privilege escalation
• Establish quantitative AI evaluation metrics and quality gates (e.g., classification accuracy, groundedness, hallucination rate, tool-selection accuracy, cost/latency) to measure performance across releases
• Build and maintain automated test suites (covering APIs, UI, integration, RAG and agent trajectories) to progressively transition testing toward automated continuous evaluation
• Analyze production AI interactions and failed agent journeys to identify recurring failure patterns, create regression tests from production incidents, and ensure decision auditability

What you'll need

• Bachelor's degree or above in Computer Science, Engineering, Information Technology, or a related discipline
• 5+ years of experience in software quality assurance and testing, with strong knowledge of test planning, functional, integration, regression and end-to-end testing
• 1–2 years of hands-on experience specifically testing GenAI, LLM or Agentic AI applications
• Strong experience with test automation, building automation frameworks and test utilities (rather than relying solely on manual tools)
• Strong programming ability in at least one language: Java / Kotlin or Python
• Strong understanding of generative AI technologies, including LLMs, prompt engineering, RAG, embeddings, vector databases, AI agents/agentic workflows, and tool/function calling
• Hands-on experience with LLM and agent evaluation tools such as Langsmith/Langfuse, Ragas, or equivalent frameworks for dataset-based testing, custom evaluation metrics, experiment tracking, tracing and continuous regression evaluation

Nice to have

• Experience testing API and data integrations, including REST APIs, JSON, SQL, databases and messaging systems like Kafka
• Good understanding of CI/CD integration and establishing automated quality gates
• Experience in the financial services industry, with knowledge of regulatory reporting, trade lifecycles, transaction reporting, reconciliations and regulatory controls
• Experience with AI/LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Vertex AI, OpenAI, or similar technologies
• Hands-on experience with advanced AI testing capabilities such as prompt injection testing, AI evaluation/evals, and AI observability
• Experience testing distributed systems
• Proven ability to leverage AI tools to enhance productivity and optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs

Details

• Location: Pune, India
• Corporate title: AVP