AI Engineer at Professional Galaxy AB

Finspång, , Sweden -

Full Time

Start Date

Immediate

Expiry Date

20 May, 26

Salary

0.0

Posted On

19 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Applied AI, Proof of Concepts (POCs), Engineering Process Improvement, AI/ML Implementation, Data Quality, Process Optimization, AI/ML Solutions, LLMs, Classification Models, Engineering Business Requirements, AI Pipelines, PLM Tools, Swedish, English, Customer Focus, End-User Education

Industry

IT Services and IT Consulting

Description

Professional Galaxy is an IT and technology consulting company that provides highly specialized expertise within IT, software development, SAP, purchasing, electronics and mechanical design. We collaborate with experienced senior experts and deliver strategic value-creating expertise to some of Sweden's most complex and analytically demanding projects. Our focus is always on high quality, professionalism and clear, measurable results. We are now seeking an AI Engineer. Requirement Profile Applied AI Engineer to support building Proof of Concepts (POCs) that help engineers in an industrial environment. Role Description Join our dynamic team as an Applied AI Engineer specializing in engineering process improvement and AI/ML implementation. You will play a key role in making engineering processes more efficient and improving data quality from concept to retirement. You will work independently or in small teams within cross-functional projects to analyze business needs, optimize processes, and implement innovative AI/ML solutions that increase efficiency and product quality. If you are passionate about driving impactful improvements across the entire product lifecycle, this role is for you. What You’ll Do Collaborate with cross-functional teams to gather and analyze engineering business requirements Map and understand engineering processes and prepare AI pipelines to improve efficiency and data accuracy Support implementation, continuous improvement, and end-user education of AI/ML systems and tools The ideal candidate is comfortable developing practical AI/ML implementations in an industrial environment and integrating LLMs and classification models into engineering development processes. Degree in Computer Science or Engineering Ability to translate engineering needs into technical requirements and rapid IT solutions Hands-on experience with industry-standard Product Lifecycle Management (PLM) tools and processes Excellent communication skills in Swedish and English Customer-focused mindset Ability to train and support end users If you are passionate about engineering data management and eager to contribute to future tools and processes, we would love to hear from you. Requirements Mandatory: Swedish and English Onsite: 5 days per week during the first 2 months, then minimum 3 days per week Experience: Minimum 2 years of applied AI pipeline implementation Department: Development & Standards / PMO Office / Engineering Systems Start date: 2026-04-13 End date: 2026-10-30 Application deadline: 2026-02-22 Are you the right person for the assignment, or would you like to recommend a strong candidate? Please apply directly through our system with: Your updated CV in English Your availability to start the assignment In your motivation, describe why you are suitable for this assignment. Please refer to previous consulting assignments, employment, education, and personal qualities. Please note: We do not accept applications via email. All applications must be submitted through the portal to be valid. Ongoing selection: For this role, we work with continuous selection. The assignment may be closed before the deadline if the right candidate is found. If you are interested, we recommend applying as soon as possible.

Responsibilities

The role involves collaborating with cross-functional teams to analyze engineering business requirements, mapping processes, and preparing AI pipelines to enhance efficiency and data accuracy. Responsibilities also include supporting the implementation, continuous improvement, and end-user education of AI/ML systems and tools.