Embedded Software Application Engineer-AI / Machine Learning Software Engineer
Aurora Engineering AB Gothenburg, Sweden
IT Services and IT Consulting · 11-50 employees
About the role
Develop and implement AI/ML pipelines that integrate Large Language Models with conventional software automation to improve engineering efficiency. Collaborate with cross-functional teams to configure and adapt machine learning solutions for specific engineering use cases.
What they look for
Requirements
Requires an MSc degree in Data Science, Computer Science, Software Engineering, or a related field. Candidates must possess strong software engineering skills, proficiency in Python, and hands-on experience with LLMs and generative AI.
Full description
We are looking for an AI / Machine Learning Software Engineer to join a team spearheading the integration of Artificial Intelligence into engineering and software development systems.
The team has established an AI platform that can be configured for different applications, primarily focused on improving software development efficiency. In this role, you will develop intelligent pipelines that combine conventional software automation with Large Language Models (LLMs) to assist or automate engineering tasks.
You will work closely with development teams, help configure and train the machine learning platform for specific use cases, and demonstrate tangible results through an agile, outcome-focused approach.
Key Responsibilities
- Develop and implement AI/ML pipelines to assist or automate engineering and software development tasks.
- Combine traditional software automation techniques with LLM-based solutions.
- Contribute to the development and continuous improvement of the organization's AI platform.
- Configure and adapt machine learning solutions for different engineering applications and use cases.
- Collaborate closely with software and engineering teams to understand requirements and develop practical AI solutions.
- Experiment with new AI/ML approaches, evaluate their effectiveness, and turn successful concepts into working solutions.
- Develop clean, scalable, maintainable, and well-designed software.
- Work in an agile environment, with a strong focus on delivering and showcasing implemented results.
- Communicate technical concepts, progress, and results effectively to both technical and non-technical stakeholders.
- Stay updated on developments in AI, machine learning, LLMs, and software engineering and identify opportunities to apply them to real-world engineering challenges.
Required Qualifications
- MSc degree in Data Science, Computer Science, Software Engineering, Machine Learning, or a related field.
- Strong software engineering and software design skills.
- Excellent programming skills, particularly in Python.
- Hands-on experience working with Large Language Models (LLMs) and generative AI technologies.
- Understanding of machine learning concepts and practical ML application development.
- Strong English communication skills, both written and verbal.
- Excellent collaboration and interpersonal skills with the ability to build strong relationships across teams.
- A curious and proactive mindset with a willingness to experiment, test ideas, and learn new technologies.
Desired Skills & Experience
- Familiarity with containerization technologies, particularly Docker.
- Knowledge of Kubernetes and cloud technologies.
- Experience working with DevOps practices and tools.
- Familiarity with automotive software development or system engineering.
- Experience integrating AI/ML solutions into existing software engineering environments.
- Understanding of CI/CD, automation, and modern software development practices.
Personal Attributes
We are looking for someone who:
- Is curious, innovative, and eager to explore emerging AI technologies.
- Takes initiative and proactively identifies opportunities for improvement.
- Is comfortable experimenting with new approaches and learning through practical implementation.
- Enjoys working collaboratively with different development and engineering teams.
- Is responsible, delivery-focused, and committed to stakeholder expectations.
- Can adapt to different technical challenges and application areas while maintaining a strong foundation in software engineering and machine learning.
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