AI Engineer (m/f/d)

Meyandy LLC; Berlin; Vor Ort; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

Raisin, the world-leading fintech platform for savings and investments, seeks an AI Engineer to shape how AI is applied across the organization. You will collaborate with product and engineering teams to identify AI use cases and build scalable, reusable capabilities while ensuring security and regulatory compliance.

You will help establish patterns for integrating LLM capabilities, contribute to shared components, and guide teams on cost, latency, and governance to enable practical AI across

Aufgaben

• Enable teams to adopt AI effectively: identify/refine AI use cases with product/engineering; provide guidance on patterns, trade-offs, and implementation approaches; promote reusable solutions to reduce fragmentation across teams.
• Contribute to shared capabilities: develop or extend components/services to unblock teams; define patterns for integrating LLMs; manage AI costs and provide access to capabilities across teams.
• Guide technical decisions: evaluate tools with emphasis on scalability, security and maintainability; help select solutions by context; promote pragmatic AI usage.
• Ensure robustness and responsible usage: optimize cost-performance, latency, and token usage; implement safeguards and access controls; collaborate with Security, Legal and stakeholders; ensure reliability and compliance.

Jobbeschreibung

Raisin is the world's leading platform for savings and investment products. Founded in 2012, theFinTech connects consumers with banks in the EU, the UK and the US. This gives consumers better interest rates and banks a diversified form of refinancing. Our vision is to offer savings and investments without barriers and thus open up the global 160 trillion euro market.

Raisin currently employs more than 800 people from over 75 countries worldwide. Today, the platform holds over 80 billion euros in assets frommore than one million investors which have accrued over 5 billion euros in returns.

Team

The AI Enablement team supports the adoption of AI capabilities across Raisin's products and internal systems.

The team focuses on establishing a consistent and reliable approach to using AI in production by providing shared foundations, guidance, and reusable solutions. This includes working with engineering teams to integrate AI into existing systems, ensuring solutions are scalable, maintainable, and aligned with security and regulatory requirements.

The scope ranges from improving internal workflows to enabling new product capabilities, depending on where AI can deliver measurable value.

Your Responsibilities

As an AI Engineer, you will help shape how AI is applied across the organization by combining hands-on engineering with enablement and guidance.

Enable teams to adopt AI effectively

• Work with product and engineering teams to identify and refine AI use cases
  
• Support teams in integrating AI capabilities into their systems and workflows by providing clear guidance on patterns, trade-offs, and practical implementation approaches
  
• Promote consistent and reusable solutions to reduce fragmentation across teams

Contribute to shared capabilities where needed

• Develop or extend shared components, integrations, or services when required to unblock teams
  
• Define and implement patterns for integrating LLM capabilities into the platform
  
• Contribute to shared components or lightweight services that simplify access to AI capabilities across teams
  
• Manage AI costs and budget usage when providing shared model access and services to other teams.
  
• Support teams in developing retrieval-based approaches that make internal knowledge accessible to AI-driven use cases

Guide technical decisions

• Evaluate tools, platforms, and approaches with consideration for scalability, security, and maintainability
  
• Help teams choose appropriate solutions based on context rather than applying AI by default
  
• Contribute to establishing a pragmatic and consistent approach to AI usage

Ensure robustness and responsible usage

• Optimize AI system efficiency by making principled model selections based on cost-performance trade-offs, inference latency, and token usage optimization
  
• Support the implementation of safeguards such as access controls, validation, and monitoring
  
• Collaborate with Security, Legal, and other stakeholders on requirements related to AI usage
  
• Contribute to practices that ensure solutions are reliable and aligned with internal standards