Enterprise Architect

Asymbl is The Workforce Orchestration Company™. We help staffing and recruiting firms bring people, AI, and software together as one hybrid workforce. We create the software, we put the AI to work, and we have the consulting expertise to orchestrate it all.

Position Overview

You'll set the architecture, data, and security direction for the platform behind our products, and you'll own the reasoning behind every major technical decision, written down so the engineers who follow know why, not just what. This isn't a whiteboard-only role. A senior engineering team executes within the architecture you set, and we're hiring more of those engineers alongside you, so your decisions need to be clear, documented, and able to hold up at real scale.

The work sits where enterprise systems meet AI-native architecture, and the problems are concrete: AI matching over large, messy, real-world talent data; digital workers that operate inside customer systems with real permission boundaries, where a misconfigured tool can leak context outside its intended scope and we treat that as a known risk class, not a hypothetical; integrations into the tools people already work in; and an AI market that changes fast enough that your choices need to stay reversible. You'll have a direct line to leadership and a team that ships what you design.

What You'll Own

• The end-to-end architecture for the platform: application, data, integration, and security, aligned to where the business is going.
• Data architecture for a data-intensive platform, including which data stores fit which problems, schema versioning, migration governance, and scalability planning.
• The architecture, evaluation, and observability approach for AI systems in production, including how our digital workers operate safely inside defined permission boundaries.
• Integration architecture patterns, including authentication models, event flow, and build-versus-platform-native decisions, for connecting our products to the systems our customers already use.
• Security and data governance review across the platform, least privilege by default.
• Architecture Decision Records that engineers actually follow.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field. A master's is a plus, not a requirement, and equivalent hands-on experience matters as much as the degree. If you've architected real systems at scale, we want to hear about them.
• 10+ years of software engineering experience, with 4+ years in an architecture-level role (Enterprise Architect, Platform Architect, Principal Engineer, or equivalent) setting technical direction that other engineers execute against.
• 3+ years designing and scaling data-intensive platforms across the full range of modern data stores (relational, document, graph, vector, and search), including schema migration governance and data sync architecture.
• 2+ years of hands-on work with LLM-based systems in production: multi-agent architectures and orchestration, evaluation frameworks including retrieval-augmented generation, and provider integration. You've made the call between a platform's native agent tooling and a custom framework and can explain when each fits.
• Experience selecting models and providers for a use case on cost, latency, and quality, and designing integrations so those choices can change without a rewrite: routing, fallback for outages and rate limits, and cost-per-token discipline. Naming providers isn't the bar, having designed for this is.
• Working depth in agent-tool integration standards (Model Context Protocol or equivalent), real depth rather than passing familiarity: tool scoping, permission boundaries, and context isolation.
• Integration architecture fluency: webhook and event-driven design, OAuth and token-based authentication, API design.
• Deep, hands-on experience with managed data, serverless, messaging, and ML services on more than one major cloud provider, including budget and cost governance.
• A security-by-default mindset and a track record of owning security and data governance decisions, not just implementing them.
• A track record of documented architecture decisions (ADRs or equivalent) that teams actually followed.
• Comfort working with teams in the US across time zones.
• Relevant certifications are welcome but optional: Google Cloud Professional Cloud Architect, AWS Certified Solutions Architect, Azure Solutions Architect Expert, Salesforce Application or System Architect, TOGAF.

Nice to Have

• Salesforce platform experience.
• Experience with agent development kits.
• Prior work on talent, recruiting, or workforce technology platforms.