BizOps Developer (Agentic AI)
eToroVisit eToro website
eToro is a social investing platform for trading and managing stocks, ETFs, crypto and other assets.
Tel Aviv, Israel
Funding history
Projects
About eToro
eToro is a social investing platform (etoro.com) offering stocks, ETFs, crypto and other assets to retail users worldwide, with a real and long-standing crypto business (eToro Money crypto wallet).
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design, build, and deploy production AI agents and applications. You will develop RAG pipelines, agentic workflows, backend services, APIs, integrations, and internal tools. You will evaluate and optimize systems for performance, reliability, scalability, and cost efficiency.
Requirements
- Two or more years of software engineering experience in full-stack or backend development
- Experience building or contributing to production AI or LLM-based systems
- Experience with RAG systems, embeddings, and retrieval pipelines
- Familiarity with LangChain, LangGraph, Semantic Kernel, or similar agent frameworks
- Experience with tool calling, structured outputs, and workflow orchestration
- Backend development skills in APIs, services, and integrations
- Experience with AI-native development tools such as Cursor or Claude Code
- Familiarity with cloud environments, preferably Azure
- Understanding of scalability, reliability, and observability
- Problem-solving and cross-functional collaboration skills
- Experience with multi-agent systems or advanced orchestration patterns is desirable
- Familiarity with LLM evaluation frameworks is desirable
- Experience with vector databases and large-scale data pipelines is desirable
- Exposure to BizOps, operations, or process automation is desirable
- Experience building internal tools or platforms is desirable
- Familiarity with event-driven or distributed systems is desirable
Responsibilities
- Design, build, and deploy AI agents and applications end-to-end
- Develop RAG pipelines for data ingestion, embeddings, retrieval, and LLM orchestration
- Implement agentic workflows using tool calling, structured outputs, and multi-step reasoning
- Build backend services, APIs, and integrations with internal systems
- Contribute to frontend and internal tools that embed AI into operational workflows
- Work with structured, unstructured, and vectorized data
- Improve system performance through evaluation, testing, and iteration
- Monitor and optimize systems for reliability, scalability, and cost efficiency
- Translate product and business requirements into practical solutions
- Contribute to shared AI infrastructure and reusable components