Machine Learning Engineering Lead, IT

at  Kemira

TITLE: MACHINE LEARNING ENGINEERING LEAD, IT

Kemira is a global leader in sustainable chemistry for water intensive industries. For more than 100 years, our chemistry has advanced human progress and quality of life. We call it chemistry with a purpose, better every day. At Kemira, we foster a collaborative and inclusive work culture that empowers our employees to thrive and make a difference. Learn more about Kemira at www.kemira.com/careers.
As part of our company-wide Data & AI development initiative, we are strengthening our Data, Analytics & AI Center of Excellence (CoE) to achieve ambitious targets. Defining and governing the best methodologies to manage the lifecycle of analytics solutions is critical for transforming exciting designs from Proof of Concept (PoC) into scaled production.
To support this, we are now looking for an experienced AI/ML Senior Engineer for our Information & Technology function.
In this role, you will collaborate with technical domain experts within Kemira to deliver optimal outcomes for AI/ML and data-driven projects. Working closely with Solution Designers, Data Scientists and Engineers, you will help to ensure the development of high-quality AI/ML services and products, from Minimum Viable Product (MVP) to full-scale production.
You can be based at any of our local offices across the EMEA region. While most of your colleagues are located at our headquarters in Helsinki, you will collaborate closely with stakeholders and peers from around the globe.

• Applying best practice Agile, MLOps, and DevOps principles to manage the lifecycle of Analytics and Advanced Analytics solutions in Kemira Azure.
• Supporting the technical implementation of solutions, including GenAI resources, AI/ML APIs, and Microservices, ensuring scalability, performance, and reliability.
• Implementing and managing infrastructure-as-code to productize AI/ML and Advanced Analytics in Cloud environments.
• Developing monitoring solutions to track model and service performance while addressing incidents related to AI/ML models and infrastructure.
• Creating and maintaining documentation for systems, processes, and procedures to ensure knowledge sharing and operational continuity.