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.
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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 LLMs, 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.
In this Role You’ll:
- Design, build, and scale backend services and RESTful APIs that power AI features.
- Train, deploy, and maintain ML models—especially in NLP and deep learning contexts.
- Stand up and iterate on LLM-driven apps, including RAG pipelines and vector-store retrieval.
- Own end-to-end delivery: prototyping, evaluation, deployment, monitoring, and iteration.
- Define clear KPIs, run experiments, and optimize based on what the data says.
- Collaborate with product, data, and engineering to turn ideas into shippable systems.
- Stay current on the LLM ecosystem and quickly put new tools and methods to work.
Requirements:
- 4+ years of professional software development experience with a strong backend focus.
- Proven track record building and deploying AI/ML models (NLP/deep learning).
- Hands-on expertise with RESTful APIs and microservices architecture.
- Practical experience with LLMs, RAG pipelines, or vector-store–based retrieval.
- Excellent communication, a bias for action, and comfort working in fast-moving teams.
Bonus Points For:
- Solid grounding in data-science fundamentals (statistics, preprocessing, feature engineering).
- Experience integrating generative models with external knowledge via RAG.
- Proficiency with embeddings and vector databases (e.g., FAISS, Pinecone).
- Working knowledge of LLM workflows: prompt engineering, inference, fine-tuning, and evaluation.
- Familiarity with Agile practices and a habit of staying on top of the latest AI research and tools.
