AI Engineering Squad Lead

Description

Who We’re Looking For – The Team Lead & Architect

We’re looking for an experienced, hands-on AI Engineering Squad Lead to lead the team building the AI agents, intelligent workflows, and decision-making systems powering Chargeflow’s products.

This role combines technical and architectural leadership with people leadership. You’ll lead engineers, build alongside them, and own production systems that must operate reliably, securely, and at scale.

Our AI platform brings together models, data, tools, business logic, and intelligent workflows to solve complex problems across the chargeback and fraud lifecycle. These systems must understand context, make decisions, and take action in real time and under uncertainty.

This isn’t a pure infrastructure or AI research role, and it isn’t about building impressive demos. It’s about turning rapidly evolving AI capabilities into reliable, production-grade products with measurable business impact. If you’re looking to manage tasks and stay above the code, this probably isn’t the role for you.

About Chargeflow

Chargeflow is a leading force in fintech innovation, tackling chargeback fraud and the impact it has on online businesses. Born from a deep passion for technology and eCommerce, we’ve developed an AI-driven solution that helps merchants manage credit card disputes, recover lost revenue, and protect their businesses through a unique success-based model.

Backed by $49M from Viola Growth, OpenView, Sequoia Capital, and other top-tier global investors, Chargeflow is on a product-led growth journey. We’re a tight-knit team of passionate builders and entrepreneurs, united by our mission to revolutionize eCommerce and protect online businesses from chargeback fraud.

What You’ll Own

• Lead, mentor, and develop a team of strong software engineers while remaining deeply involved in architecture and code.
• Own the architecture and evolution of Chargeflow’s AI platform.
• Design and build production-grade AI agents, intelligent workflows, and decision-making systems.
• Combine LLMs with business logic, company data, external tools, and deterministic software to solve complex product problems.
• Own AI systems throughout their lifecycle, from design and implementation to deployment, evaluation, monitoring, and continuous improvement.
• Make thoughtful architectural tradeoffs across accuracy, latency, scalability, reliability, security, and cost.
• Turn ambiguous product and business problems into clear technical direction, milestones, and measurable outcomes.
• Build evaluation frameworks that measure system quality, correctness, reliability, and business impact.
• Raise the bar for code quality, system design, observability, and engineering execution.
• Review designs and code, challenge assumptions, and help strong engineers become exceptional.
• Partner closely with Product, Data, and other R&D teams to turn emerging AI capabilities into meaningful customer value.
• Help define how engineering teams at Chargeflow build software with AI while maintaining high standards for correctness, security, and maintainability.

Requirements

What What You Bring

• At least seven years of software engineering experience, with a strong background in backend engineering, platform engineering, or distributed systems.
• At least three years of experience leading engineers, including mentoring, technical direction, delivery, and performance ownership.
• Hands-on experience shipping LLM- or generative-AI-powered capabilities to production—not only proofs of concept.
• Practical experience with agentic systems, tool use, orchestration, RAG, evaluations, and AI observability.
• Experience designing, building, and operating complex distributed or event-driven systems at scale.
• Strong architectural judgment and the ability to balance execution speed with reliability and long-term quality.
• A hands-on leadership style: you still write, review, and challenge code.
• The ability to connect technical decisions with product, customer, and business outcomes.
• Experience owning production systems beyond launch, including monitoring, incident response, performance, and continuous improvement.
• An ownership mindset that extends beyond your immediate team or formally defined scope.
• Strong communication skills and the ability to create clarity in technically complex and ambiguous environments.

Nice to Have

• Experience building AI systems for real-time decision-making or other high-stakes use cases.
• Experience in fintech, payments, fraud, chargebacks, or another high-volume transactional environment.
• Experience with MCP, complex tool-calling systems, or multi-agent orchestration.
• Experience developing evaluation frameworks for non-deterministic AI systems.
• Experience introducing AI-native development practices across an engineering team or organization.

Our Stack

Our current stack includes:

• Node.js, TypeScript, and Python.
• LLM APIs, AI agents, tool calling, RAG, and MCP.
• Distributed and event-driven systems.
• Kafka and AWS.
• MongoDB and PostgreSQL.
• Docker, Kubernetes, and ECS.
• AI evaluation, monitoring, and observability tooling.

The stack is provided for context, not as a checklist. We care more about strong engineering fundamentals, architectural judgment, and the ability to learn than exact tool matching.

How We Work

AI is both part of our product and part of how we build it. We actively use tools such as Codex, Claude Code, and Cursor in our daily engineering workflows, while applying human judgment where correctness, security, and maintainability matter most.

Our Squad Leads don’t simply manage tasks and run standups. They build, make technical decisions, develop engineers, and take responsibility for the systems their teams operate in production.

This is a hybrid role based in our Modi’in office, with three days a week in the office.

Compensation

We offer highly competitive compensation for exceptional candidates.

If you want to build, lead, and rethink how production software is created in the age of AI, we’d love to hear from you.