Lead Java​/Kotlin​/AI Engineer

N-iX is looking for Lead Java/Kotlin/AI Engineer to join the team

Our client, headquartered in California, is a global e-commerce leader connecting millions of buyers and sellers in over 190 markets. This is a high-impact individual contributor role focused on shaping, building, and scaling advanced AI systems at one of the world’s most established and heavily trafficked ecommerce platforms.

You will be expected to provide strong technical leadership through architecture, design reviews, code reviews, mentorship, technical strategy, cross-team influence, and hands-on execution.

As a Lead Engineer, you will work across the full AI lifecycle: early exploration, rapid prototyping, system design, model and agent integration, evaluation, experimentation, production deployment, observability, and continuous improvement.

Your focus will include Generative AI systems, LLM-powered applications, intelligent agents, conversational AI, retrieval-augmented generation, agentic workflows, and multi-agent architectures. You will help define the technical direction for key AI initiatives while directly working on sophisticated engineering work.

This role requires deep hands-on engineering ability, strong architectural judgment, AI systems expertise, and the ability to lead through influence rather than formal authority. You should be comfortable operating in ambiguous problem spaces, making pragmatic tradeoffs, influencing senior partners, and helping teams turn ambitious AI ideas into durable production systems.

• Lead the architecture, design, development, and optimization of scalable AI systems using Generative AI, LLMs, retrieval-augmented generation, and agent-based architectures.
• Serve as the technical owner or technical lead for high-impact AI initiatives that span multiple services, systems, teams, or product surfaces.
• Design and build agent-led user experiences and backend systems that leverage task decomposition, memory, tool use, dynamic planning, retrieval, workflow orchestration, and multi-agent coordination.
• Translate ambiguous business, research, and product opportunities into clear technical strategies, architecture proposals, implementation plans, milestones, risks, and tradeoffs.
• Drive architectural decisions for AI-powered products and platforms, ensuring systems are reliable, maintainable, scalable, cost-effective, observable, and production-ready.
• Contribute directly to complex implementation work across backend services, AI orchestration layers, model integration systems, evaluation frameworks, APIs, data pipelines, and observability tooling.
• Partner with Product, Research, Data Engineering, Platform Engineering, and Software Engineering teams to align AI system design with user needs, business goals, platform capabilities, and operational constraints.
• Establish and promote technical standards for AI system design, agent architecture, LLM integration, evaluation, experimentation, responsible AI, production monitoring, and operational excellence.
• Lead design reviews, architecture reviews, code reviews, technical planning sessions, and production-readiness reviews for complex AI systems.
• Mentor and guide engineers through technical problem solving, design feedback, implementation support, code quality improvements, and knowledge sharing.
• Help advance the internal GenAI platform by contributing reusable components, APIs, frameworks, reference architectures, evaluation patterns, engineering guidelines, and shared services.
• Define and improve AI evaluation practices, including offline evaluation, online experimentation, regression testing, model behavior analysis, quality measurement, human feedback loops, and production feedback mechanisms.
• Monitor and optimize AI…