Dive deeper. Aim higher.
At Abysalto, that’s not just a motto — it’s how we work.
We build serious AI-powered systems used in real production environments, often with high expectations around reliability, security, and correctness. As our AI products and teams grow, quality becomes just as critical as innovation.
That’s why we’re looking for an AI QA Engineer who understands how AI systems fail, how to test them properly, and how to help teams ship AI features with confidence.
Ready to dive in?
🔷 What will you do?
- Design and implement quality assurance strategies for AI-powered systems, including LLM-based applications, ML models, and data pipelines
- Define test plans and acceptance criteria for AI features (functional, non-functional, and edge-case behavior)
- Test AI systems end-to-end: APIs, model inference, data flows, prompts, and integrations
- Develop and maintain automated tests for backend services and AI workflows (API tests, regression tests, smoke tests)
- Validate model outputs for correctness, robustness, bias, hallucinations, and failure modes
- Collaborate closely with AI Backend Developers, ML Engineers, and Data Engineers to catch issues early
- Help define quality metrics for AI systems (accuracy thresholds, latency limits, stability, reproducibility)
- Participate in incident analysis: reproduce issues, identify root causes, and help prevent regressions
- Continuously improve QA processes, tooling, and documentation as products scale
🔷What do we expect from you?
- Experience in software testing or quality engineering, preferably in backend or data-heavy systems
- Solid understanding of REST APIs, HTTP, JSON, and backend architectures
- Hands-on experience with test automation (e.g. pytest, unittest, Postman/Newman, or similar tools)
- Basic programming skills in Python (reading code, writing tests, simple scripts)
- Ability to reason about AI/ML system behavior, even if you haven’t trained models yourself
- Understanding of common AI failure modes: non-determinism, hallucinations, data drift, edge cases
- Experience working with Git, CI/CD pipelines, and Linux environments
- Strong attention to detail, analytical thinking, and a structured approach to problem solving
- Good communication skills in English — you can explain issues clearly and constructively
🔶 Nice to have
- Experience testing LLM-based systems (RAG pipelines, prompt-based applications, chatbots)
- Familiarity with ML concepts (classification, embeddings, inference vs training, evaluation metrics)
- Experience with performance and load testing (latency, throughput, resource usage)
- Knowledge of data validation and testing data pipelines
- Exposure to MLOps practices (model versioning, monitoring, rollback strategies)
- Experience in regulated or enterprise environments where quality really matters
🔷What do we offer?
- A key role in shaping quality standards for AI products across the company
- Close collaboration with experienced AI engineers and tech leads
- Supportive environment for growth, learning, and specialization in AI quality engineering
- Modern tech setup — high-performance hardware, ergonomic workstations (Aeron chairs), GPU access
- Flexible working hours with a hybrid work option
- Agile workflows with SCRUM methodology (flexible) and a strong engineering culture
- All perks and benefits available on our career page
- A chance to help build AI systems that millions of people rely on
We solve complex technological challenges in order to simplify and improve everyday lives of millions of people. Our goal is to become a leader in the software industry, recognized for excellence and quality.
If you're ready to shape what’s yet to be — send us your CV.
Apply via the link below.
We look forward to meeting you!