Prompt Engineer (Backend, GenAI)

Description

Who we are?

Surecomp® is the market-leading provider of digital trade finance solutions for corporates and financial institutions. An industry pioneer for more than thirty-five years, we enable seamless, sustainable trade by breaking down barriers and fostering collaboration. Our award-winning portfolio of cloud and on-premises solutions streamlines the trade finance lifecycle for frictionless transaction processing, enhanced risk mitigation, compliance, decision support, and growth. Underpinned by global offices and an extensive partner network, our prestigious customer footprint spans over eighty countries across the world. For more information visit surecomp.com. 

Our Vision and Mission

We believe that Sustainable global trade promotes growth and improves people’s lives. The better the world trades, the better society is.

Our Mission is to enable seamless sustainable trade for everyone by removing barriers and facilitating collaboration.

Introducing RIVO

RIVO™ - our collaborative trade finance platform is a centralized digital hub through which any trade finance instrument can be managed to enhance efficiency and optimize customer service. This web-based portal provides open API access to every trade participant for a completely streamlined and sustainable transaction process. For more information on RIVO please visit rivo.trade

About the Role

RIVO is Surecomp’s flagship digital trade finance SaaS platform, serving global financial institutions with mission critical workflows. 

We are looking for a Backend Prompt Engineer with 4+ years of backend experience and proven hands on delivery of GenAI powered features in production environments. 

This is not a research or experimentation role. Prompt engineering at RIVO is system design. You will design reliable, scalable, production ready LLM workflows that power real customer facing capabilities inside a complex distributed platform. 

You will work closely with backend engineers, product managers, and architects to integrate LLM based intelligence into core business flows. 

Location: Kfar Saba, Israel

Reporting to: AI Team Lead

Roles and Responsibilities

• Design, implement, and continuously improve prompts for LLM driven product features.
• Architect and develop backend services in Python.
• Integrate LLM APIs such as OpenAI, Anthropic, and AWS Bedrock into production systems.
• Implement structured output enforcement, schema validation, and response normalization.
• Design robust error handling, fallback strategies, retries, and resiliency mechanisms.
• Optimize latency, token usage, throughput, and API cost efficiency.
• Build evaluation frameworks and quality control pipelines for AI outputs.
• Collaborate with Product and Engineering teams to deliver AI features end to end within RIVO.

Requirements

Knowledge and Experience 

• 4+ years of backend development experience with strong proficiency in Python.
• Proven hands on experience building and shipping GenAI powered features to production.
• Strong experience with Python GenAI frameworks such as LangChain, LangGraph, Strands, or similar orchestration frameworks.
• Experience integrating LLM APIs into live distributed systems.
• Experience implementing structured outputs, validation layers, and guardrails.
• Familiarity with evaluation frameworks and LLM quality measurement techniques.
• Experience building RESTful APIs.
• Strong understanding of clean architecture, scalability, and production best practices.

Advantages

• Experience designing and implementing RAG pipelines.
• Experience with MCP servers, A2A architectures, or multi agent systems.
• Experience working with embeddings and vector databases.
• Proficiency in TypeScript (Node.js or ReactJS).
• Experience with AI observability, monitoring, and evaluation tooling.

What We Are Looking For 

A strong backend engineer who: 

• Has integrated LLMs into real production systems.
• Understands that prompt engineering is part of a larger distributed architecture.
• Knows how to design reliable systems around probabilistic models.
• Thinks in terms of validation, monitoring, resilience, and system stability.
• Is motivated to deepen expertise in AI system design and orchestration.