Were hiring an AI Engineer to join a newly formed AI Engineering group dedicated exclusively to RIVO, our flagship SaaS platform transforming global trade finance.
This is a high-impact role in a lean team with a clear mission: deliver AI-powered product capabilities end-to-end, from idea and prototype to production rollout, monitoring, and iteration. Were not experimenting with AI; were building real product features and laying the AI foundation that will power RIVO for years to come.
If youre a backend engineer who has built AI/LLM solutions in production and wants to shape the AI ecosystem inside a global SaaS product – this role is for you.
Location: Kfar Saba, Israel
Reporting to: AI Team Lead
Roles and Responsibilities:
Own AI features end-to-end: translate product problems into AI solutions, prototype quickly, productionize, and continuously improve based on real usage and metrics.
Build and maintain production-grade AI services using Python and/or Node.js, integrated into RIVOs backend architecture.
Develop the AI foundation for RIVO: shared building blocks, standards, templates, APIs, evaluation tooling, guardrails, and observability.
Design and implement prompting strategies, function/tool calling flows, and structured output patterns to achieve reliable results.
Build and optimize RAG pipelines (retrieval, ranking, chunking, context construction) and integrate with vector databases and hybrid retrieval techniques.
Implement evaluation and monitoring: offline testing, automated regression suites, online metrics, cost monitoring, and quality dashboards.
Work closely with Product, Engineering, and Domain Experts to ensure solutions are scalable, secure, and aligned with business value.
Research and adopt the best-fit models and approaches (OpenAI/Anthropic/open-source), including routing, fallback strategies, and cost/performance optimization.
Write clean, scalable, well-tested, and well-documented code.
This is a high-impact role in a lean team with a clear mission: deliver AI-powered product capabilities end-to-end, from idea and prototype to production rollout, monitoring, and iteration. Were not experimenting with AI; were building real product features and laying the AI foundation that will power RIVO for years to come.
If youre a backend engineer who has built AI/LLM solutions in production and wants to shape the AI ecosystem inside a global SaaS product – this role is for you.
Location: Kfar Saba, Israel
Reporting to: AI Team Lead
Roles and Responsibilities:
Own AI features end-to-end: translate product problems into AI solutions, prototype quickly, productionize, and continuously improve based on real usage and metrics.
Build and maintain production-grade AI services using Python and/or Node.js, integrated into RIVOs backend architecture.
Develop the AI foundation for RIVO: shared building blocks, standards, templates, APIs, evaluation tooling, guardrails, and observability.
Design and implement prompting strategies, function/tool calling flows, and structured output patterns to achieve reliable results.
Build and optimize RAG pipelines (retrieval, ranking, chunking, context construction) and integrate with vector databases and hybrid retrieval techniques.
Implement evaluation and monitoring: offline testing, automated regression suites, online metrics, cost monitoring, and quality dashboards.
Work closely with Product, Engineering, and Domain Experts to ensure solutions are scalable, secure, and aligned with business value.
Research and adopt the best-fit models and approaches (OpenAI/Anthropic/open-source), including routing, fallback strategies, and cost/performance optimization.
Write clean, scalable, well-tested, and well-documented code.
Requirements:
3-6 years of backend development experience in Python and/or Node.js.
2+ years building AI/LLM-based solutions in production (not just prototypes).
Demonstrated ability to take AI solutions from idea → production, including reliability, monitoring, iteration, and stakeholder alignment.
Strong understanding of LLM capabilities, limitations, and best practices (prompt design, tool/function calling, structured outputs, hallucination mitigation).
Experience integrating AI systems into real backend services (REST APIs, auth, async workflows, event-driven patterns).
Strong engineering fundamentals: system design, testing, versioning, scalability, maintainability.
Advantages:
Experience with AWS (Lambda/ECS/EKS, API Gateway, S3, etc.).
Hands-on experience with vector databases and embeddings (Pinecone, Weaviate, Qdrant, OpenSearch, pgvector).
Strong understanding of RAG and hybrid retrieval (BM25 + embeddings, reranking, filters, metadata strategies).
Experience with LLM evaluation frameworks and tooling (prompt/unit tests, golden sets, offline evals, A/B testing).
Familiarity with open-source LLMs and deployment patterns (vLLM, llama.cpp, model quantization).
Skills :
Builder mindset: pragmatic, execution-focused, and comfortable working in ambiguity.
Strong ownership and accountability – you ship, you measure, you improve.
Excellent communication and collaboration skills.
Strong organizational and prioritization abilities in a fast-moving environment.
Proficient spoken and written English
3-6 years of backend development experience in Python and/or Node.js.
2+ years building AI/LLM-based solutions in production (not just prototypes).
Demonstrated ability to take AI solutions from idea → production, including reliability, monitoring, iteration, and stakeholder alignment.
Strong understanding of LLM capabilities, limitations, and best practices (prompt design, tool/function calling, structured outputs, hallucination mitigation).
Experience integrating AI systems into real backend services (REST APIs, auth, async workflows, event-driven patterns).
Strong engineering fundamentals: system design, testing, versioning, scalability, maintainability.
Advantages:
Experience with AWS (Lambda/ECS/EKS, API Gateway, S3, etc.).
Hands-on experience with vector databases and embeddings (Pinecone, Weaviate, Qdrant, OpenSearch, pgvector).
Strong understanding of RAG and hybrid retrieval (BM25 + embeddings, reranking, filters, metadata strategies).
Experience with LLM evaluation frameworks and tooling (prompt/unit tests, golden sets, offline evals, A/B testing).
Familiarity with open-source LLMs and deployment patterns (vLLM, llama.cpp, model quantization).
Skills :
Builder mindset: pragmatic, execution-focused, and comfortable working in ambiguity.
Strong ownership and accountability – you ship, you measure, you improve.
Excellent communication and collaboration skills.
Strong organizational and prioritization abilities in a fast-moving environment.
Proficient spoken and written English
This position is open to all candidates.
