Innovation and Technology Specialist
Indexed description
Our aim is to shape a better future for our planet through supporting the energy transition, (bio)technology, artificial intelligence, industrial cybersecurity, etc. We are committed to the United Nations sustainable development goals by utilizing our ability to measure and connect.
About The Team
Our 18,000 employees work in over 60 countries with one corporate mission, to "co-innovate tomorrow". We are looking for dynamic colleagues who share our passion for technology and care for our planet. In return, we offer you great career opportunities to grow yourself in a truly global culture where respect, value creation, collaboration, integrity, and gratitude are highly valued and exhibited in everything we do.
Job Description
- Design, develop, and deploy AI/ML-based applications using Python and modern frameworks (TensorFlow, PyTorch, scikit-learn)
- Implement backend services (REST APIs, FastAPI, Flask, Django) and frontend interfaces (React.js, Dash/Plotly, Angular, Vue)
- Develop business cases for adopting new technologies, assessing feasibility, risks, and ROI
- Collaborate cross-functionally with data scientists, domain engineers, and product managers to define solution requirements
- Integrate AI modules with industrial systems (IoT sensors, OPC-UA, SCADA, MES)
- Support go-to-market strategies for commercially viable technology solutions
- Stay current on industry trends through research, conferences, and workshops
- Master’s/Ph.D. in Computer Science, Data Science, AI/ML, Engineering, or related quantitative field
- Chemical/Instrumentation engineering background with software development and AI engineering may also be considered
- 8+ years of hands-on ML modeling experience architecting, building, fine-tuning, and deploying models
- Strong expertise in AI/ML frameworks and libraries (TensorFlow, PyTorch, scikit-learn)
- Proficient in programming languages such as Python, .NET, Java, or C++.
- Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes)
- Deep knowledge of ML algorithms, deep learning, NLP, LLMs, RAG pipelines, time series forecasting, and computer vision
- Familiarity with industrial AI/OT systems, protocols, and sensor data integration
- Working knowledge of MLOps tools (MLflow, CI/CD, Docker) and DevOps practices across Windows/Linux environments
- Commitment to ethical and responsible AI design and deployment
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