Bebo Technologies - Software Engineer - AI/ML Testing

Actively Reviewing the Applications

bebo Technologies

India, Odisha Full-Time On-site INR 8–30 LPA

Posted 3 weeks ago • Apply by March 21, 2026

Job Description

Description

BE, B.Tech, M.
Tech, MCA or equivalent degree in Computer Science (or related field).
3-5 years of QA experience, with at least 2 years focused on AI/ML testing (LLMs, RAG, AI agents).
Proficiency in Artificial Intelligence, Machine Learning, AI Agents, and Large Language Models (LLMs).
Strong knowledge of QA methodologies, test design, functional/non-functional testing, and defect lifecycle management.
Solid understanding of LLM evaluation, hallucination types, prompt behavior, response scoring, and quality metrics.
Good understanding of RAG concepts including vector databases, embeddings, and retrieval relevance.
Coding proficiency in Python or Java (both preferred).
Hands-on experience in Web/API automation using frameworks like Playwright, Selenium, or REST Assured.
Experience in writing automation scripts, maintaining test frameworks, and integrating test suites into CI/CD pipelines.
Ability to analyze large sets of AI outputs for patterns and systemic issues.
Excellent analytical reasoning, problem-solving, and communication skills.

Job Responsibilities

Test and validate LLM outputs, ensuring accuracy, correctness, completeness, consistency, usability, and hallucination analysis.
Evaluate RAG systems, including retrieval accuracy, document relevance, context construction, and full response generation flows.
Test AI agents and autonomous workflows, validating decision-making, task execution, and error handling.
Design and execute AI-specific test strategies : dataset creation, edge-case testing, adversarial testing, pattern-based testing, and regression validation.
Develop evaluation frameworks, scoring rubrics, and benchmarking models for AI quality assessment.
Analyze large volumes of AI-generated responses to identify patterns, root causes, and issue clusters, instead of isolated defects.
Knowledge of testing conversational AI, workflows, or agent-based systems.
Exposure to vector search tools and embedding quality analysis.
Validate fixes using new examples from the same pattern category, ensuring true model improvement.
Collaborate closely with AI/ML engineers, QA teams, and product managers to improve AI accuracy and performance.
Contribute to continuous improvement of AI QA practices, automation, tools, and evaluation datasets.

(ref:hirist.tech)