Senior AI Engineer
⚲ Brussels
Do uzgodnienia
Wymagania
- Security
- Python
- Use Cases
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Microsoft Azure
- Governance
- Deployment
- Data Science
- PySpark
Opis stanowiska
The Senior AI Engineer plays a vital role in translating business needs into actionable AI solutions, focusing on design, deployment, and robust machine learning applications within an e-commerce context.
Responsibilities:
• Translate business needs into clear analytical and AI problem statements.
• Design, build, validate, deploy, and operate AI / ML solutions in production.
• Develop scalable data and AI pipelines to support customer analytics and reporting use cases.
• Industrialize analytical models ensuring robustness, scalability, and cost efficiency.
• Collaborate with business stakeholders and product-oriented teams for adoption and value delivery.
• Apply AI engineering, MLOps, governance, security, and compliance standards.
• Contribute to reusable components, best practices, and continuous improvement of AI capabilities.
Must Haves:
• Minimum 3 years of relevant experience in AI / ML engineering or data science.
• Proven experience in Data Science / Machine Learning.
• Strong experience designing and deploying production-grade AI / ML solutions end-to-end.
• Hands-on experience with Azure, Databricks, PySpark, and Python.
Nice to Haves:
• Experience in e-commerce or omnichannel environments.
• Good understanding of MLOps, deployment, orchestration, monitoring, and operationalization of AI solutions.
• Experience in customer analytics, behavioral analysis, and segmentation.
Other Details:
• Team Structure: Collaborative with business and product teams.
• Work Environment: Fast-paced operational environment.
Responsibilities:
• Translate business needs into clear analytical and AI problem statements.
• Design, build, validate, deploy, and operate AI / ML solutions in production.
• Develop scalable data and AI pipelines to support customer analytics and reporting use cases.
• Industrialize analytical models ensuring robustness, scalability, and cost efficiency.
• Collaborate with business stakeholders and product-oriented teams for adoption and value delivery.
• Apply AI engineering, MLOps, governance, security, and compliance standards.
• Contribute to reusable components, best practices, and continuous improvement of AI capabilities.
Must Haves:
• Minimum 3 years of relevant experience in AI / ML engineering or data science.
• Proven experience in Data Science / Machine Learning.
• Strong experience designing and deploying production-grade AI / ML solutions end-to-end.
• Hands-on experience with Azure, Databricks, PySpark, and Python.
Nice to Haves:
• Experience in e-commerce or omnichannel environments.
• Good understanding of MLOps, deployment, orchestration, monitoring, and operationalization of AI solutions.
• Experience in customer analytics, behavioral analysis, and segmentation.
Other Details:
• Team Structure: Collaborative with business and product teams.
• Work Environment: Fast-paced operational environment.
🔍 Dekoder Ogłoszenia
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translating business needs into actionable AI solutions
Oznacza to, że będziesz musiał przekształcać ogólne prośby biznesowe w konkretne zadania związane z AI, co może wymagać dużej ilości komunikacji i doprecyzowania.
🔴
design, build, validate, deploy, and operate AI / ML solutions in production
To szeroki zakres obowiązków, który może oznaczać, że będziesz odpowiedzialny za cały cykl życia modelu, od koncepcji po utrzymanie w produkcji, co może być bardzo czasochłonne.
🔴
Industrialize analytical models ensuring robustness, scalability, and cost efficiency
Może sugerować, że obecne modele nie są jeszcze zindustrializowane, a Ty będziesz musiał je dopracować, co może być trudnym zadaniem.
🟡
Collaborate with business stakeholders and product-oriented teams for adoption and value delivery
Wskazuje na konieczność częstej interakcji z osobami spoza zespołu technicznego, co może wymagać umiejętności tłumaczenia skomplikowanych zagadnień technicznych na język biznesowy.
🔴
Apply AI engineering, MLOps, governance, security, and compliance standards
Choć brzmi profesjonalnie, może oznaczać, że firma dopiero wdraża te standardy, a Ty będziesz musiał pomóc w ich ustanowieniu i egzekwowaniu.