Senior AI Testing Engineer (Generative AI)

Wfnen; Bengaluru; On-site; INR 1,200,000 - 1,600,000; Full time

Job summary

Wfnen in Bengaluru is seeking a Senior AI Testing Engineer to ensure quality across Generative AI products. This role involves designing evaluation frameworks and building automated testing pipelines. A strong emphasis is on engineering quality into AI systems, requiring 5–8 years in software testing and 2–3 years in Generative AI.

The ideal candidate will collaborate with AI engineers and product teams to drive quality standards and will need expertise in test automation using Python, along with solid knowledge of LLM systems.

Qualifications

• 5–8 years in software testing, QA engineering, SDET, or test automation.
• 2–3 years of hands-on experience testing Generative AI or LLM systems.
• Strong test automation skills in Python.

Responsibilities

• Design comprehensive testing strategies for Generative AI products.
• Define evaluation methodologies covering functional and safety testing.
• Build AI testing frameworks and automation pipelines.

Skills

Software testing

QA engineering

SDET

Test automation in Python

API testing

Performance testing

Job description

Bangalore North, India | Posted on 06/01/2026

Location: India Remote / Hybrid / In-office — [specify your actual working model here]

Experience: 5–8 years total in software testing, QA engineering, or SDET roles, with at least 2–3 years of meaningful, hands‑on exposure to Generative AI systems, LLM applications, or AI quality engineering.

Role Overview

We are looking for a Senior AI Testing Engineer to own quality across our Generative AI products and platform.

This role is fundamentally about engineering quality into AI systems — not running test scripts. You'll design evaluation frameworks, build automated testing pipelines, and define what "good" looks like for LLM outputs, RAG systems, AI agents, and voice AI applications. You'll work directly with AI engineers and product teams to make sure our systems are reliable, safe, and measurably improving over time.

If you understand how LLMs fail, know how to catch hallucinations before users do, and want to build the quality infrastructure that underpins production AI at scale — this is the role.

Key Responsibilities

• Design and own comprehensive testing strategies for Generative AI products — including LLM applications, RAG pipelines, AI agents, voice AI systems, and workflow automation
• Define evaluation methodologies covering functional testing, response quality, hallucination detection, safety and guardrail testing, prompt injection, bias and toxicity, retrieval quality, latency benchmarking, and agent workflow validation
• Build reusable AI testing frameworks and automation pipelines for continuous evaluation
• Create datasets, benchmark suites, and golden test sets for GenAI evaluation

Automated Evaluation

• Develop automated evaluation pipelines using LLM-as-a-Judge and hybrid evaluation methods
• Implement CI/CD–integrated AI evaluation pipelines
• Drive observability and monitoring strategies for production AI systems

Quality Standards & Collaboration

• Define measurable quality KPIs for AI systems
• Establish testing standards, best practices, and governance processes for GenAI applications
• Work closely with AI engineers, product, and platform teams to embed quality throughout the development lifecycle

Required Skills & Experience

• 5–8 years in software testing, QA engineering, SDET, or test automation
• 2–3 years of hands‑on experience testing or evaluating production‑grade Generative AI or LLM‑based systems
• Strong test automation skills in Python
• Experience designing scalable automated testing frameworks
• Familiarity with API testing, integration testing, and performance testing

Generative AI Knowledge

• Solid understanding of how LLM systems work — and how they fail
• Experience with RAG architectures, prompt engineering, AI agents, embedding models, and vector databases
• Understanding of LLM evaluation methodologies and AI system failure modes

GenAI Testing Frameworks

• Hands‑on experience with at least one or more GenAI evaluation frameworks, such as: DeepEval, Ragas, LangSmith, Promptfoo, TruLens, OpenAI Evals, or LangChain evaluation tools

Quality Engineering

• Expertise in test strategy, test planning, test automation architecture, defect lifecycle management, and quality metrics
• Ability to define and track measurable quality KPIs for AI systems

Preferred Qualifications

• Experience with cloud platforms (AWS, Azure, or GCP)
• Familiarity with MLOps / LLMOps workflows
• Experience with CI/CD pipelines and DevOps practices
• Exposure to monitoring and observability tooling for AI systems
• Understanding of security and compliance for GenAI products
• Experience with conversational AI or voice AI systems