Tech Manager AI

About Nimbus Data Systems Nimbus Data Systems is a Consumer Tech Platform for Transformative Brand Growth. We’re building the AI Growth Engine that powers the next generation of consumer brands. With over $1B+ in cumulative revenue, 12M+ customers, and 500+ talents across 19 countries, we combine technology, growth marketing, and operational excellence to scale purpose-driven, digitally native brands. We own and operate our own digitally native brands (not an agency), with presence in Walmart, Nordstrom, and Amazon, and a strong global D2C footprint. Why Nimbus Data Systems We’re a tech-led eCommerce group scaling our own globally loved DTC brands, while helping ambitious talent grow just as fast. 🚀 We build and scale our own brands. 🦾 We invest in AI and automation like few others in eCom. 📈 We test fast, grow fast, and help you do the same. 🤝 Be part of a dynamic, diverse, and talented global team. 🌍 100% Remote, USD competitive salary, paid time off, and more. Job Responsibilities This is a builder role, not a manager role . You will: Architect and scale AI systems in production (LLM agents, recommendation engines, personalization) Design multi-agent workflows and AI microservices that operate at scale Turn business problems into AI-driven solutions with measurable impact Ship AI features that directly improve conversion, retention, and customer experience Build and lead a high-performance AI team (2–5 hires per quarter) Establish MLOps, CI/CD, monitoring, and scalable AI infrastructure Requirements 5+ years leading engineering teams in high-performance, fast-paced environments 3+ years building and shipping AI/ML systems in production (LLMs, recsys, personalization, or similar) Hands-on experience with LLMs, AI APIs, and frameworks (LangChain or similar) Strong experience designing scalable architectures, microservices, and AI pipelines Proven track record of delivering AI solutions tied to business impact (CVR, revenue, UX) Experience with AWS and cloud-based AI infrastructure Solid understanding of MLOps (deployment, monitoring, model lifecycle, performance) Experience hiring, mentoring, and scaling engineering teams Strong ability to connect technical decisions with business outcomes Advanced English (global, remote team) Originally posted on Himalayas