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.

About the Role:

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.

 Responsibilities:

• 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 systems in production for latency, quality, scalability, reliability, availability, cost efficiency, safety, and responsible AI use.
• Identify technical risks early and drive practical mitigation plans across architecture, implementation, launch, and operations.
• Influence technical strategy across teams by aligning engineering decisions with broader organizational goals, platform direction, and long-term scalability.
• Stay ahead of advances in AI, LLMs, machine learning engineering, AI agents, retrieval systems, model serving, evaluation tooling, and emerging developer frameworks, applying a pragmatic lens to production adoption.
• Drive continuous improvement across design, implementation, evaluation, deployment, monitoring, incident response, and operational processes.

Requirements:

• 10+ years of experience in software engineering, machine learning engineering, AI engineering, distributed systems, or related technical roles.
• 3+ years of experience leading complex technical initiatives, setting architecture, mentoring engineers, or providing technical direction across teams.
• Deep hands-on engineering skills, with the ability to personally design and implement complex production systems.
• Strong programming skills in Java, Kotlin or similar JVM languages
• Experience designing and operating production-grade AI systems, distributed services, APIs, or platform components that serve high-volume, real-world user traffic.
• Ability to lead technical discussions, evaluate tradeoffs, influence senior partners, and communicate complex AI concepts clearly to technical and non-technical collaborators.
• Hands-on experience with: Spring Framework, Reactive Programming, Docker and Kubernetes, Distributed systems and scalable backend services, REST, GraphQL, gRPC, or other service/API patterns, Production monitoring, observability, alerting, incident response, and performance optimization, CI/CD, automated testing, deployment practices, and operational support
• Excellent English communication skills

Nice to have:

• Hands-on experience building agent-led systems, including LLM-based agents, autonomous workflows, task-planning systems, tool-using agents, retrieval-augmented agents, or multi-agent orchestration.
• Strong understanding of Generative AI system design, including prompt orchestration, model integration, retrieval, grounding, tool use, evaluation, guardrails, observability, and production monitoring.
• Experience with retrieval-augmented generation, vector databases, embeddings, semantic search, ranking, knowledge-grounded AI, or information retrieval systems.
• Knowledge of tool-use frameworks, agent orchestration platforms, AI workflow automation, multi-modal model integration, or multi-agent system design.
• Experience building internal AI platforms, developer tools, reusable AI services, model-serving infrastructure, evaluation platforms, or shared ML infrastructure.
• Experience operating AI systems in high-traffic ecommerce, marketplace, search, personalization, recommendations, ads, trust, risk, payments, or customer-service environments.
• Experience defining responsible AI practices, model governance processes, safety evaluation, abuse-prevention patterns, or production AI monitoring standards.
• Experience with streaming data systems such as Kafka.