Principal Architect

ABOUT EMA Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver and Bangalore, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. WHO WE ARE Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver and Bangalore, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. THE ROLE As a Principle Architect at Ema, you will build and lead a high-performance engineering organization — setting the technical standard for reliability and demonstrating a strong execution mindset. You will collaborate directly with the Head of Engineering and co-founders to architect and drive the product roadmap from an engineering standpoint. We are looking for a leader who combines deep, hands-on technical expertise with the executive presence required to attract, mentor, develop, and retain top-tier engineering talent. WHAT YOU'LL DO TEAM BUILDING & ENGINEERING CULTURE - Recruit, hire, and develop senior engineers across multiple sub-teams: cloud infrastructure, data platform, ML operations, and developer experience. - Establish engineering standards, code review culture, on-call expectations, and a bias-toward-shipping mentality balanced with production rigor. - Coach and grow senior/staff engineers into technical leaders; manage engineering managers as the organization scales. TECHNICAL STRATEGY & EXECUTION - Set the 6–18 month platform roadmap in partnership with engineering teams, balancing foundational investments against product-driven urgency. - Make high-stakes architectural decisions — build vs. buy, migration strategies, technology bets — and own the outcomes. - Drive cross-functional alignment with product, ML/AI research, and go-to-market teams to ensure the platform evolves in lockstep with customer and product needs. OPERATIONAL EXCELLENCE - Own production health for all platform services: incident response, postmortems, SLO tracking, and capacity planning. - Establish and iterate on engineering practices that keep the team shipping fast without compromising reliability. - Participate in executive-level reviews on infrastructure spend, system health, and engineering velocity. WHAT YOU BRING REQUIRED - 12+ years of software engineering experience, with 4+ years leading platform or infrastructure teams of 8+ engineers at high-growth startups or top-tier tech companies. - Deep, hands-on expertise in distributed systems. - Production experience with container orchestration (Kubernetes), microservices architecture, and at least two major cloud providers (GCP, AWS, Azure). - Strong programming ability in Go and Python; you still read code daily and can mentor engineers on systems-level design. - Experience building quality software using AI-driven tools. - Track record of building internal platforms from scratch that other engineering teams adopt and depend on. - Expertise in database internals and performance tuning across SQL, NoSQL, and graph or vector data stores; working knowledge of CAP theorem trade-offs in production. - Proven ability to hire, develop, and retain high-caliber engineers in competitive markets. STRONGLY PREFERRED - Experience building infrastructure for AI/ML workloads: model serving, GPU scheduling, training pipelines, or inference optimization. - Familiarity with vector databases, embedding pipelines, RAG architectures, or LLM orchestration frameworks. - Experience operating multi-tenant SaaS platforms with strict data isolation and compliance requirements (SOC 2, GDPR, HIPAA). - Background in real-time data streaming (Kafka, Pulsar, Flink) and event-driven architect