Principal Solutions Architect

Pre-SOW technical validation Partner with Sales, Engagement, and Solutions Engineering to validate platform fit, integration complexity, data/security constraints, FDE effort, and implementation risks before strategic SOWs are finalized.

Production architecture for strategic accounts Design reference architectures for multi-channel AI agent systems across voice, chat, email, and SMS, including integrations with CCaaS, CRM, data warehouse, identity, and internal knowledge systems. Define data contracts, tool/action patterns, observability, privacy boundaries, fallback behavior, and acceptance gates.

Technical direction from POC to launch Guide FDEs through architecture decisions, edge cases, CI/CD, evals, monitoring, rollback plans, and launch readiness. You own technical direction and architecture quality; FDEs own implementation.

Reusable deployment leverage Turn each strategic deployment into reusable assets: reference architectures, integration templates, eval harnesses, implementation checklists, demo baselines, or product requirements that reduce effort for future launches.

Executive and technical customer communication Lead architecture reviews, security reviews, roadmap sessions, and technical deep dives with VP/C-level sponsors, security teams, and staff engineers. Make trade-offs clear for both executive and deeply technical audiences.

Field-to-product feedback loop Identify repeated customer needs and implementation gaps. Partner with Product and Engineering to turn field patterns into roadmap inputs, reusable platform capabilities, and core product improvements.

Within your first 6–12 months:

Strategic deals have clearer technical feasibility, integration scope, risk, and effort estimates before SOW.

FDE teams move faster because they have stronger architecture guidance, launch gates, and reusable implementation assets.

Complex deployments launch with better observability, evals, fallbacks, and privacy controls.

Customers trust Maven’s technical direction in security, architecture, and executive-level reviews.

Repeated deployment patterns become productized rather than remaining bespoke.

7+ years in Solutions Architecture, Applied Engineering, Forward Deployed Engineering, Sales Engineering, or equivalent customer-facing technical leadership for enterprise SaaS, data, ML, or AI products.

Track record taking complex enterprise technical solutions from discovery or POC into production.

Strong system design skills across APIs, event-driven systems, data modeling, identity/consent flows, latency, throughput, reliability, and observability.

Practical AI/LLM depth: RAG, tool use/function calling, prompt and retrieval evaluation, safety guardrails, agent orchestration, and quality measurement.

Hands-on technical fluency in TypeScript/Node, Python, or similar. You are not the primary production engineer, but you can prototype, review code, debug integrations, and reason through implementation trade-offs.

Excellent written, verbal, and visual communication. You can lead a security review, produce a clear architecture diagram, and present trade-offs to executives.

Builder mindset. You want to help create the Solutions Architecture function, not just operate inside an existing one.

Contact center / CCaaS experience with platforms like Genesys, Five9, NICE, Twilio, or Amazon Connect.

Depth with Zendesk, Salesforce Service Cloud, ServiceNow, or similar customer support systems.

Experience in regulated or complex data environments such as financial services, healthcare, gaming, marketplaces, or franchise businesses.

Experience at a frontier AI company, FDE-style company, data/ML platform company, high-growth SaaS company, or as a technical lead on complex enterprise AI programs.

Prior experience building a technical function from zero to one.