AI Software Engineer

Job description / Role

Rapyd company overview

Rapyd has unified payments, payouts, and fintech on one worldwide platform, and we're assembling the world's best team to liberate global commerce. With offices in Tel Aviv, Amsterdam, Singapore, Iceland, London, Dubai, Hong Kong, and the U.S., the opportunities at Rapyd are limitless.

We believe in straight talk, quick decisions, strong execution, and elegant solutions. Rapyd is where hard work pays off and careers take off. Join us and let's build the future of fintech together.

Role overview

We're looking for a builder with a backend backbone - someone who can spot an opportunity, sketch a solution, and ship a working service fast. You'll turn AI ideas into production-grade APIs and microservices, then iterate based on real data and feedback.

You'll work at the edge of what's possible with large language models (LLMs), retrieval-augmented generation (RAG) pipelines, and vector-based retrieval. The mission: wire up smart systems that automate workflows, elevate productivity, and make AI feel less like magic and more like reliable infrastructure.

Responsibilities

• Design, build, and scale backend services and RESTful APIs to power AI-driven features.
• Develop and iterate on LLM-based applications, including context-aware pipelines such as vector-store retrieval.
• Own the full lifecycle: prototyping, evaluation, deployment, monitoring, and continuous iteration.
• Define key performance indicators (KPIs), run experiments, and optimize systems based on metrics and data.
• Collaborate across product, data, and engineering teams to translate ideas into robust, shippable systems.
• Stay current with the evolving LLM and agent ecosystem and rapidly apply new tools or protocols.

Core requirements

• Five or more years of professional software development experience with a strong backend focus.
• Experience with advanced LLM workflows including prompt engineering, inference optimization, fine-tuning, and evaluation.
• Demonstrated experience integrating generative models with external knowledge via RAG or similar patterns.
• Practical knowledge of embeddings and vector databases such as FAISS or Pinecone.
• Experience building and scaling data pipelines for LLM ingestion and retrieval.
• Strong communication skills, bias for action, and ability to thrive in fast-paced environments.

Bonus and preferred qualifications

• Previous work with agent frameworks or context protocols.
• Experience with Agile practices and a habit of staying on top of AI research and tooling.
• Hands-on experience designing and building microservices architectures.