Applied AI Engineer

Role overview

This opportunity is being advertised on behalf of a partner organization, which will handle the application review and all subsequent hiring steps. The employer is seeking an Applied AI Engineer located in Germany.

This is a strategic engineering position centered on creating the core systems that support AI and machine learning across the business. You will operate where platform engineering meets applied AI, building the infrastructure that allows teams to develop, launch, and run LLM- and ML-driven products reliably and at scale. Your contributions will influence how AI solutions are accessed, tracked, assessed, and governed in live environments. Instead of owning one product only, you will design shared platform capabilities that multiple engineering and product teams will rely on. The setting is fast-paced, collaborative, and technically demanding, with a strong focus on dependability, scalability, and an excellent developer experience. This role suits engineers who like solving complex system challenges and helping others ship better AI products.

Key accountabilities

• Develop and improve platform-layer services such as LLM routing or proxy systems, internal APIs, and reusable AI/ML utilities.
• Advance LLM and ML operations capabilities, including monitoring, observability, evaluation, and deployment processes.
• Support AI systems throughout their lifecycle, from testing and scaling to optimization and production stability.
• Work closely with product, infrastructure, and data teams to promote consistent and reusable AI development patterns.
• Contribute to architecture and design choices that enhance performance, cost efficiency, safety, and response latency for AI services.
• Assess and bring in new AI models, frameworks, and tools to strengthen shared platform features.

Requirements

• Minimum 4 years of software engineering experience, including hands-on work with production environments.
• At least 1 year spent in MLOps, LLMOps, or AI/ML infrastructure-focused roles.
• Background in building backend services, internal platforms, or developer tools used by engineering teams.
• Strong grasp of the complete ML/LLM lifecycle, especially deployment and live operations.
• Solid engineering judgment with the ability to manage trade-offs among reliability, scalability, latency, cost, and maintainability.
• Strong communication and collaboration abilities with a team-first approach.
• Comfort working with, or openness to learning, TypeScript and Python in backend and platform environments.

Benefits and workplace support

• Competitive pay structure with base salary, variable compensation, and equity.
• Broad healthcare coverage covering medical, dental, and vision needs.
• Flexible work setup with a remote-first or hybrid-friendly culture.
• Unlimited paid leave plus generous parental leave provisions.
• Learning and development allowance to encourage ongoing professional growth.
• Home office stipend and equipment assistance.
• Mental health and wellness support resources.
• Chance to contribute to advanced AI infrastructure operating at scale.

Hiring process and privacy notice

The application is handled through an AI-assisted matching process designed to review candidates quickly, fairly, and consistently against the role’s main requirements. The system identifies the most suitable profiles, and that shortlist is then passed directly to the hiring employer. Final hiring decisions, including interviews and assessments, are managed by the employer’s internal team.

By applying, you agree that the platform will process your personal data to assess your candidacy and share relevant details with the hiring company. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws, including GDPR. You may exercise your data rights at any time, including access, correction, deletion, and objection.

The hiring process may also use artificial intelligence tools to support tasks such as application review, resume analysis, and response assessment, including checks for potential inconsistencies or verification signals in submitted materials. These tools support the recruitment team and do not replace human judgment. Final hiring decisions are made by people. For more information about how your data is handled, you may contact the recruiter.