3 дня назад
Senior Engineer – GenAI Quality Assurance (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior Engineer – GenAI Quality Assurance (AI) (GenAI/LLM): Designing, testing, and refining GenAI applications, pipelines, and AI-assisted workflows with an accent on automated QA, evaluation pipelines, model behavior, grounding, robustness, and safety. Focus on building synthetic test data and test harnesses, validating LLM systems across AWS, Azure, and GCP, and embedding quality checkpoints throughout continuous integration and deployment.
Location: Hybrid role based in Madrid or Barcelona, Spain
Company
Johnson & Johnson MedTech develops healthcare and medical technology solutions, while Innovative Medicine R&D focuses on treatments and digital health products.
What you will do
- Design and implement automated and manual QA strategies for GenAI systems.
- Build regression tests, synthetic test data, test harnesses, and evaluation pipelines for accuracy, grounding, robustness, and safety.
- Validate prompt logic, model behavior, evaluation metrics, APIs, data, and pipelines; execute tests and debug failures.
- Support deployment and post-release monitoring of GenAI products and AI-assisted workflows.
- Lead system design reviews, code reviews, and quality standards focused on architecture, reliability, testability, and maintainability.
- Collaborate with data scientists, developers, domain experts, and stakeholders across the product lifecycle.
Requirements
- At least 3 years of software engineering or QA engineering experience in production systems; GenAI experience is preferred.
- Strong software engineering background with QA ownership and advanced Python skills.
- Experience with LLM-based systems, LLM APIs, RAG and graph frameworks, and GenAI test frameworks.
- Hands-on experience with AWS Bedrock, Azure Foundry, GCP Gemini, and cloud platforms including AWS, Azure, or GCP.
- Knowledge of API testing, data validation, data pipeline testing, Git, scientific computing, and machine learning frameworks.
- Understanding of GenAI safety, bias, failure modes, and full lifecycle AI/ML product development.
Nice to have
- Experience with Jenkins, SonarQube, Docker, Prometheus, Grafana, DevOps, MLOps, or production ML systems.
- Experience deploying GenAI services to production at scale.
Culture & Benefits
- Hybrid working arrangement in Madrid or Barcelona.
- Collaborative work with data scientists, developers, scientific experts, and healthcare stakeholders.
- Fast-moving environment that values initiative, independent problem-solving, and teamwork.
- Work focused on healthcare innovation across scientific and digital health applications.
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