DESCRIPTION

You’re excited about the potential of foundation models — and not just in theory. You want to work with cutting-edge AI systems and bring them into production: building custom solutions, integrating them into complex applications, and making them work for users. At SQUER, you’ll consult, delivering impactful solutions directly for and with our clients across industries. If you combine strong software engineering skills with a deep interest in AI, we’d love to hear from you.

YOUR QUALIFICATIONS

• Software Engineering: Extensive experience in Java or TypeScript with a strong focus on clean code and architecture.
• Python & ML Frameworks: Bonus if you’re proficient in Python and familiar with frameworks like TensorFlow, PyTorch, or similar.
• Backend Experience: Solid knowledge of backend technologies (e.g., Java, Node.js) and RESTful API development.
• CI/CD & Architecture: Deep understanding of modern development workflows, architecture patterns, and delivery pipelines.
• Foundation Model Know-how: Practical experience working with or adapting foundation models in real-world applications.
• Cloud Knowledge: Familiarity with AWS, GCP, or Azure, especially their AI/ML capabilities.
• Data Engineering: Experience with ETL workflows, feature engineering, and scalable data processing.
• MLOps Familiarity: Understanding of model deployment, monitoring, and lifecycle management in production.
• Mindset: Problem-solving-oriented, hands-on, and comfortable working in agile, interdisciplinary teams.

ABOUT US

We are dedicated to creating a supportive workplace where everyone can thrive and be proud to be part of the team. We encourage our colleagues to ask questions, try new things, pursue ongoing professional development, and take calculated risks.
Are you ready to learn and collaborate in independent agile teams? We are looking forward to meeting you

• AI Engineering: Design and develop robust, scalable, and maintainable software solutions integrating machine learning models.
• Foundation Models: Work with existing foundation models and develop custom AI solutions tailored to client needs.
• Software Craftsmanship: Apply engineering best practices to ensure quality, performance, and security in AI-driven systems.
• System Integration: Collaborate with cross-functional teams to embed AI capabilities in broader software landscapes.
• ML Pipelines: Optimize training, inference, and deployment workflows across the full model lifecycle.
• Internal Tools: Contribute to internal libraries and tools that accelerate the delivery of AI-based services.
• AI Strategy: Help shape the direction of AI at SQUER through hands-on expertise and strategic input.