AI Platform Engineering Team Lead

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

Chargeflow is at the forefront of fintech and AI innovation. Our mission is to build a fraud-free global commerce ecosystem by leveraging cutting-edge technology—freeing online businesses to focus on their core ideas and growth.

We’re looking for an experienced, hands-on AI Platform Engineering Team 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.

This Is Not Your Typical Leadership Role

If you’re looking to manage tasks, run standups, and stay above the code—this position probably isn’t for you.

But if you want to build AI systems, shape engineers, and redefine how software teams operate in the age of AI—keep reading.

What We’re Actually Building

At Chargeflow, we’re building systems that don’t just process data—they understand context, make decisions, and take action.

Real-time. At scale. Under uncertainty.

Our AI Platform brings together models, data, tools, business logic, and intelligent workflows to solve complex problems across the chargeback and fraud lifecycle.

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.

What You’ll Actually Do

• Lead a team of strong software engineers—and build alongside them
• Own the architecture and evolution of Chargeflow’s AI Platform
• Build production-grade AI agents, intelligent workflows, and decision-making systems
• Combine LLMs with business logic, data, tools, and deterministic software
• Own AI systems across their lifecycle—from design and implementation to deployment, evaluation, and continuous improvement
• Make architectural trade-offs across accuracy, latency, scalability, reliability, security, and cost
• Turn ambiguous product and business problems into clear technical direction
• Raise the bar for code quality, system design, and engineering execution
• Help strong engineers become exceptional
  
  

• 7+ years of software engineering experience, with a strong backend, platform, or distributed systems background
• 3+ years of experience leading engineers, including mentoring, technical direction, and performance ownership
• Hands-on experience shipping LLM- or GenAI-powered capabilities to production—not just proofs of concept
• A practical understanding of agentic systems, tool use, orchestration, RAG, evaluations, and AI observability
• Experience building and operating complex, distributed, or event-driven systems at scale
• Strong architectural judgment and the ability to balance speed with long-term quality
• A hands-on mindset—you still write, review, and challenge code
• The ability to connect technical decisions with product, customer, and business outcomes
• Ownership that extends beyond your immediate team or defined scope
  
  

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