eToro is the trading and investing platform that empowers users to invest, share, and learn. We were founded in 2007 with the vision of a world where everyone can trade and invest simply and transparently. We have created an investment platform that is built around collaboration and investor education. On our platform, users can view other investors’ portfolios and statistics, and interact with them to exchange ideas, discuss strategies, and benefit from shared knowledge. We have 40 million registered users from 75 countries, and our platform is available in 20 languages. eToro is an innovative, fast-growing business and is listed on Nasdaq in May 2025. We have over 1,500 employees across more than 10 offices around the globe, strategically positioned to serve the needs of users. You can find out more about eToro here.
Role Summary
We’re hiring a BizOps AI Squad Lead to build and lead a high-impact engineering squad focused on AI-powered operational systems and agentic applications.
This is a hands-on leadership role where you will own the end-to-end delivery of AI solutions - from discovery and architecture to production rollout and adoption. You will design and build scalable AI agents, internal platforms, and automation systems that enhance decision-making, streamline operations, and unlock efficiency across the organization.
You will operate at the intersection of AI engineering, full-stack development, and business operations, partnering closely with Product and Ops stakeholders while actively contributing to code, architecture, and technical direction.
What will you be doing?
Lead a small squad (2 BizOps Developers) delivering agentic AI solutions for BizOps and operational domains
Own end-to-end delivery: problem discovery, solution design, implementation, evaluation, and rollout
Design and build AI agents and applications, including RAG pipelines, tool-calling systems, and multi-agent workflows
Define and drive technical direction for; Agent frameworks and orchestration patterns, RAG architecture and data pipelines, and Evaluation frameworks, quality metrics, and benchmarking
Establish best practices for reliability and quality for; Testing and evaluation (accuracy, regression, guardrails), and Monitoring, observability, and continuous improvement
Build full-stack systems (APIs, backend services, and user-facing tools) that integrate AI into daily operations
Integrate with internal systems (CRM, data platforms, third-party tools) to enable automation and decision intelligence
Partner closely with business stakeholders to prioritize use cases, define requirements, and drive adoption
Act as a hands-on technical leader: contribute code, review implementations, and guide architecture decisions
Mentor and unblock engineers while maintaining high engineering standards and velocity