
Machine Learning Solutions Architect
4 days ago
We are seeking a highly skilled Machine Learning Solutions Architect to join our Data Science and Optimization team. As a Machine Learning Solutions Architect, you will be responsible for designing and implementing cutting-edge AI solutions that drive innovation and efficiency in our business.
The successful candidate will have a strong background in machine learning, MLOps, and software engineering, with experience working with cloud solutions, particularly Azure and/or AWS. You will work closely with our multidisciplinary team of designers, developers, data scientists, and testers to design and automate stable MLOps pipelines, communicate with diverse stakeholders, and contribute to best practices development in AI solutions and MLOps.
Main Responsibilities:
- Design and implement AI and ML solutions in Fortum's cloud environments (both Azure and AWS) that are robust, scalable, secure, and cost-efficient.
- Design and automate stable MLOps pipelines with model re-training.
- Contribute to best practices development in AI solutions and MLOps.
- Communicate with diverse stakeholders and understand the business needs.
- Discuss and design interfaces with backend and frontend teams.
- Discuss requirements for storage and data maintenance protocols with DevOps, CloudOps, EA architecture, and cybersecurity functions.
- Coach the team and be open to learn about the energy domain.
About You:
To succeed in this role, you should have an MSc degree in Computer Science, Engineering, or equivalent work experience. A strong background in Machine Learning and MLOps is essential, with at least three years of solid experience in software engineering using Python. You must also be proficient in statistics and data science, having spent a minimum of two years honing these skills. Experience working with cloud solutions, particularly Azure and/or AWS, for over two years is necessary.
You should also have a keen interest in the latest AI developments and experience with GenAI, cloud AI services, and different solution concepts (e.g., RAG, model fine-tuning, prompt engineering). Familiarity with GitHub Actions service deployment, containers, and DevOps practices is also desired. Excellent communication skills and interest to learn about the energy domain are essential.
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