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Senior AI Agent Engineer
Mirakl — признанный лидер (Next40) с сильной инженерной культурой. Вакансия предлагает работу над передовыми технологиями (Agentic Commerce) в крупной международной компании с четкими процессами и современным стеком.
Сложность вакансии
Высокая сложность обусловлена требованием глубоких знаний в узкой нише (AI-агенты, LangGraph, MCP) и наличием 5+ лет опыта. Процесс отбора включает 5 этапов, включая технические интервью и проверку на соответствие культурным ценностям (STAR).
Анализ зарплаты
Предлагаемая роль Senior AI Engineer в крупной технологической компании Франции обычно оплачивается выше среднего по рынку из-за дефицита специалистов по LLM и агентам. Указанный диапазон соответствует уровню Senior в компаниях уровня Next40 в Париже/Бордо.
Сопроводительное письмо
I am writing to express my strong interest in the Senior AI Agent Engineer position at Mirakl. With over five years of experience in Machine Learning and a proven track record of deploying LLM-based solutions into production, I am excited by Mirakl’s vision of 'Agentic Commerce'. My expertise in building complex agentic workflows using LangChain and LangGraph, combined with a deep understanding of RAG architectures, aligns perfectly with your mission to reshape how users interact with marketplaces.
In my previous roles, I have focused on bridging the gap between data science prototypes and scalable software engineering. I have extensive experience with your tech stack, including Python, PyTorch, and Databricks, and I am particularly adept at implementing robust evaluation and monitoring frameworks for AI agents. I am eager to bring my technical leadership and passion for emerging AI paradigms to the Data team at Mirakl to help drive the next generation of marketplace innovation.
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Описание вакансии
About Mirakl:
Founded in 2012, Mirakl has been at the forefront of marketplace innovation, empowering every business to compete in the platform economy.
Today, Mirakl’s operating system combines an enterprise marketplace solution (Mirakl Platform) that enables retailers and B2B organizations to launch, scale, and operate marketplaces and dropship, AI-powered multichannel selling (Mirakl Connect), retail media (Mirakl Ads) and an agentic commerce infrastructure (Mirakl Nexus).
With dual headquarters in Boston and Paris, Mirakl helps a global ecosystem of 450+ marketplaces (B2C and B2B) and a network of over 100k third-party marketplace sellers. Brands like Macy’s, Decathlon, Carrefour, Asos, and Airbus Helicopters use Mirakl to grow their businesses in new and remarkable ways.
For more information: www.mirakl.com.
Mirakl in Numbers:
- 🗓️ Founded in 2012 | Member of French Tech Next40
- 👥 750+ employees in 9 offices worldwide: Paris, Barcelona, Bordeaux, Boston, London, Munich, New York, Sydney, Tokyo
- 🇫🇷 350+ Mirakl Tech teams members mainly based in France
- ⚙️ 5 Saas Solutions
Our Values:
Working at Mirakl means accelerating your career alongside ambitious, passionate, and supportive colleagues. We're proud of the diversity of backgrounds, perspectives, and experiences that make our teams unique.
Our 5 values guide how we collaborate:
- 💡 Work Hard Together: Teamwork and collaboration are the foundation of our success
- 🏆 Get Things Done: We prioritize action and efficiency for impactful results
- 🚀 Go Above & Beyond: We tackle challenges proactively and always aim for excellence
- 🎓 Succeed Through Expertise: Knowledge sharing and continuous learning are core to our culture
- 🤝 Satisfy & Empower Clients: We're committed to our clients' success
About the job:
The eCommerce landscape is entering a new phase of profound change: Agentic Commerce, where AI-powered agents autonomously search, discover, compare, and ultimately purchase on behalf of users. This evolution is transforming how customers interact with products—how they arrive, engage, how choices are filtered, and how relevance is determined in a rapidly evolving paradigm. From product discovery to marketplace optimization, these changes are reshaping retailers’ ecosystems.
In this context, as a Senior AI Engineer, you will join our Data team (60+ people) to prototype, iterate, and integrate agentic systems into Mirakl products, working closely with Data Scientists, Data Engineers, Product Managers and Software Development teams.
The position is open for a Permanent contract (CDI) based in Paris or Bordeaux with :
- 4 days onsite per week
- 1 day worked remotely per week
Our stack and tools:
Python, Tensorflow, Pytorch, Databricks, Spark, AWS, SQL, Airflow, Delta Lake
During a typical week, you will:
- Design, develop, and optimize AI agents using frameworks and tools such as LangChain, LangGraph, and LangFlow
- Leverage large language models (LLMs) and techniques such as Retrieval-Augmented Generation (RAG)
- Own the full lifecycle from initial design through MVP delivery and successive iterations
- Implement reusable frameworks & tool to accelerate development while ensuring scalability (evaluation, monitoring, testing, deployment)
- Explore emerging AI paradigms, evaluate new tools and technologies, and maintain active technology watch
You'll Love This Job If you have
- You have a minimum of 5 years of experience as a Data Scientist or Machine Learning Engineer with a strong Software Engineering skills
- Proven experience building AI agents using LLMs, RAG, MCP and related technologies with architectural design experience
- Production deployment expertise - you've shipped AI solutions in production
- Strong proficiency in Python
- Curiosity and continuous learning - you stay current with AI/ML trends and love exploring new technologies
- Strong communication skills in both English and French
Feel free to add a link to your GitHub in your CV so we can understand what you have worked on!
Want to join us? Here is our process
- A 30-minute phone conversation with one of our Tech recruiters. This will be an opportunity to discuss your background, expectations, and discover what Mirakl can offer you in return.
- An initial 30-minute technical discussion via Zoom with a member of the Data Science team, which will allow you to delve into the more specific aspects of your expertise and discover how your skills can be integrated into our projects.
- A 90-minute technical review and discussion with a manager from the Data Science team
- 2 x 45-minute "STAR" Zoom interviews with future Mirakl colleagues to discuss our values and company culture.
We welcome collaborators with their diverse perspectives and experiences to power us forward. These often far exceed conventional job requirements and help us create a culture of continuous learning. If you’re ready to join a global leader powering digital transformation for 450+ of the world’s most innovative retailers and B2B organizations..
We may use Artificial Intelligence (AI) solutions to help streamline our hiring process, including screening applications, analyzing resumes, and assessing responses. While AI helps us work efficiently, all final hiring decisions are made by humans. For more information, visit our AI Guidelines for Candidates and Interviews.
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Навыки
- Python
- TensorFlow
- PyTorch
- Databricks
- Spark
- AWS
- SQL
- Airflow
- Delta Lake
- LangChain
- LangGraph
- LangFlow
- LLM
- RAG
- MCP
Возможные вопросы на собеседовании
Проверка практического опыта работы с фреймворками для создания агентов, упомянутыми в вакансии.
Можете ли вы описать случай, когда вы использовали LangGraph или LangFlow для решения сложной задачи управления состоянием (state management) в многошаговом ИИ-агенте?
Вакансия делает упор на Agentic Commerce, где агенты совершают действия. Важно понять, как кандидат обеспечивает надежность.
Как вы подходите к проектированию систем оценки (evaluation) и тестирования для агентов, которые имеют доступ к внешним инструментам и могут изменять данные?
Упоминание MCP (Model Context Protocol) в вакансии указывает на потребность в интеграции различных источников данных.
Каков ваш опыт работы с Model Context Protocol (MCP) и как, по вашему мнению, он изменит архитектуру агентских систем в ближайшем будущем?
Роль требует сильных навыков Software Engineering, а не только Data Science.
Расскажите о вашем опыте деплоя LLM-решений в продакшн: с какими проблемами задержки (latency) или масштабируемости вы сталкивались и как их решали?
Проверка способности кандидата оптимизировать поиск информации для агентов.
В контексте RAG, какие стратегии индексации и извлечения вы считаете наиболее эффективными для работы с динамическими данными маркетплейсов?
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