Solutions Architect - SF Bay Area

As a Solutions Architect at Zilliz, you'll help AI startups and digital-native enterprises design, validate, and operate production retrieval systems on Zilliz Cloud (fully-managed Milvus). Working with engineering leaders and hands-on builders, you'll turn product goals into data models, search architectures, benchmarks, and deployment plans across RAG, hybrid and multimodal search, recommender systems, agent memory, and semantic analytics workloads.

This is a deeply technical, customer-facing role. The strongest candidates move comfortably between a whiteboard and a profiler: they understand how schema, vector dimension, filter predicates, and indexes could affect latency, recall, throughput, reliability, and cost, as well as explain those tradeoffs to engineers and business leaders alike.

What you will do:

Partner with Account Executives on technical win strategy, leading discovery on use case, traffic pattern, latency requirements, and cost constraints

Design solutions covering data modeling, multi-tenancy, latency profile, full-text search, filtering. Guide POCs and benchmarks with clear success criteria. Guiding customers to make wise trade-offs on performance, quality and cost. Translate customer growth plans into deployment and capacity strategies

Partner with engineering to troubleshoot production issues, driving customer communication, and follow up with clear root-cause analysis and durable remediation

Build trusted relationships with founders, engineering executives, ML leaders, and developers from evaluation through production and expansion

Turn field learning into reusable demos, reference architectures, and product feedback; engage with Milvus open-source community and shape the roadmap

What we are looking for:

• 5+ years in solutions architecture, solutions/customer engineering, or a comparable technical customer-facing role

• Strong foundation in distributed systems and computer science, with experience in schema design, performance benchmarking, and troubleshooting with metrics and logs

• Ability to reason about tradeoffs among performance, reliability, operational complexity and cost. Strong business judgment and customer empathy: you can distinguish the needs of a fast-moving startup, a scaled platform, and a regulated enterprise, and adapt accordingly

• Excellent written and verbal communication, including the ability to challenge assumptions constructively and lead executive and engineering conversations

• Strong sense of ownership, intellectual curiosity, and comfort in a fast-changing startup environment

Nice to have:

Experience with distributed databases, large-scale data pipelines or lakehouses.

Experience with search stack, including embedding model, full-text search, ranking and search quality.

Experience with major cloud platforms or kubernetes

How we operate:

Customer outcomes first: We start with the customer's workload and constraints, then recommend the simplest architecture that can scale.

Deep and evidence-driven: We value correctness, benchmarks, and clear reasoning over slogans, and we don't hide uncertainty.

Fast and self-learning: We're engineers at heart, not afraid to dive into code to solve customer problems and use AI to self-educate.

One team: We share field learning across Sales, Engineering, and Product, and take ownership through resolution.

Benefits:

Competitive compensation (cash + equity)
Regular bonus and equity refresh opportunities
Medical, dental, and vision insurance
Paid time off, including vacation, sick leave, and global wellbeing days
Generous 401(k) and regional retirement plans