AI/ML Research Engineer

Role overview

This opportunity is for an AI/ML Research Engineer in Germany, presented on behalf of a partner organization that handles the hiring process and all follow-up steps. The position combines applied AI research with production-focused engineering, with emphasis on agentic AI systems and large language model (LLM) applications. The work involves turning enterprise data into practical insights and autonomous workflows, while also contributing to the development of next-generation AI assistant products.

You will be part of a fast-moving, collaborative environment where experimentation with modern AI frameworks, model benchmarking across providers, and robust solution design are central to the role. The work requires both research-oriented thinking and strong hands-on engineering capability, especially in enterprise-scale data and cloud-based environments.

Responsibilities

• Architect, build, and launch scalable, reliable agentic AI systems and LLM-driven applications.
• Develop and refine AI/ML solutions in Python, including prompt design and integration with contemporary LLM tooling.
• Create AI assistant and agent-based products that can automate complex workflows and support decision-making.
• Work with large enterprise data platforms and databases such as SAP HANA, DB2, and SQL Server, using advanced SQL for processing and analysis.
• Compare and choose suitable AI models, including offerings from OpenAI, Meta, and others, based on performance and use-case fit.
• Partner with cross-functional teams on solution architecture, model integration, and cloud deployment.
• Help scale enterprise-ready AI solutions while maintaining stability, resilience, and maintainability.

Requirements

• A bachelor’s degree in Computer Science, Data Science, Systems Engineering, or a similar discipline, or equivalent practical experience.
• Strong practical experience with Python and prompt engineering for LLMs.
• Demonstrated experience building agentic AI systems or AI assistant-style applications.
• Good understanding of NLP and the deployment of scalable machine learning models.
• Advanced SQL skills and exposure to enterprise databases such as SAP HANA, DB2, and SQL Server.
• Experience working in large-scale enterprise data environments or similarly complex systems.
• Familiarity with cloud AI/ML platforms; certifications such as Azure AI Engineer or AWS ML are a strong plus.
• Exposure to Model Context Protocol (MCP), predictive modeling, or Node.js will be considered an advantage.
• Strong analytical and problem-solving abilities, with comfort working in a collaborative, fast-paced setting.
• Capability to assess trade-offs between AI models and architectures according to business and technical requirements.

Benefits

• Chance to contribute to advanced AI research and agentic system development.
• Hands-on exposure to enterprise-grade data environments and AI infrastructure.
• A collaborative engineering culture centered on innovation.
• Practical experience with leading LLM providers and emerging AI frameworks.
• Good opportunities for learning, experimentation, and career growth.
• A flexible, dynamic workplace focused on impact and innovation.

Application and data processing information

This role is managed through a partner hiring process. Applications are reviewed by an AI-supported matching system that screens candidates against the role’s core requirements before sharing a shortlist with the hiring company. Interviews, assessments, and final hiring decisions are handled internally by the employer.

By applying, candidates consent to the processing of personal data for evaluation and sharing with the hiring employer under applicable data protection rules, including GDPR, based on legitimate interest and pre-contractual measures. Applicants may exercise rights such as access, correction, deletion, and objection at any time.

Artificial intelligence tools may be used to assist with parts of recruitment, such as application review, resume analysis, and identifying possible inconsistencies in submitted materials. These tools support the recruitment team but do not replace human judgment, and final decisions are made by people.