Lead Solutions Architect, AI Build & Delivery
Foundever® United States
Outsourcing and Offshoring Consulting · 10,001+ employees
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About the role
The Lead Solutions Architect will own the end-to-end build of AI solutions, from initial client brief to production deployment. They will also lead the practice by establishing technical standards, mentoring junior staff, and partnering with Sales to scope and price new service opportunities.
What they look for
Requirements
Candidates must have 7+ years of experience in solutions architecture or software engineering with a strong track record of shipping AI-driven solutions. Proficiency in LLM application patterns, AI-assisted development tools, and the ability to operate autonomously in a zero-to-one environment is required.
Full description
About Foundever:
Foundever™ is a global leader in the customer experience (CX) industry. With 170,000 associates across the globe, we are the team behind the best experiences for +750 of the world’s leading and digital-first brands. Our innovative CX solutions, technology and expertise are designed to support the operational needs of our clients and deliver a seamless experience to customers in the moments that matter.
Job Summary
We are building an AI Professional Services practice from the ground up. We need a technically savy Lead Architect who can own the build end-to-end: from client brief to working prototype, from prototype to production, and from solo contributor to the technical anchor of a growing team.
This is a rare founding opportunity. You will personally ship the first solutions, establish what great AI delivery looks like at Foundever, and grow into a Tech Lead role as the practice scales. You will work closely with Sales, Product, and client stakeholders — bringing engineering credibility and realistic delivery judgment to every engagement.
WHAT YOU'LL DO
1. Client Delivery & Technical Build
- Be the primary builder in every early client engagement — take a scoped brief to a working prototype within days, not weeks
- Design and deliver tailored AI solutions using LLMs, agents, RAG pipelines, and AI-assisted development workflows — from discovery through go-live
- Build live demos and proof-of-concepts that directly support deal closure, working alongside commercial stakeholders
- Own technical quality across all solutions: security, scalability, maintainability, and production readiness
- Lead the transition of delivered solutions to production — including monitoring, cost control, reliability, and support handoff
- Apply responsible AI delivery standards: data privacy, model selection, prompt and version management, and safeguards where required
2. Commercial & Scoping Judgment
- Assess client needs and determine the best solution path: EverSuite capability, off-the-shelf tooling, or custom AI build
- Estimate build costs with precision — time, infrastructure, tooling, and ongoing maintenance
- Join client scoping sessions and technical discovery calls, bringing engineering credibility and realistic timelines
- Flag reusable solution patterns that can evolve into packaged service offerings
- Partner with Sales to scope, price, and validate new services opportunities from a technical standpoint
3. Product Feedback Loop
- Identify recurring custom client requests that signal mid-term product opportunities
- Communicate technical insights clearly and actionably to Product Managers
- Define what "productizable" looks like technically — providing the feasibility foundation for roadmap decisions
- Feed client insights back into the EverSuite roadmap through structured cross-functional collaboration
4. Practice Building
- Document architecture patterns, reusable components, and build playbooks from day one
- Establish delivery methodologies and quality standards for AI implementations
- Create repeatable offerings: AI discovery workshops, rapid prototyping packages, paid pilots, and managed AI solution support
- As the team grows, step into a Tech Lead role — setting technical standards, reviewing work, and mentoring junior architects and engineers
- Define technical hiring criteria for future team members
WHAT WE'RE LOOKING FOR
Must-Haves
- 7+ years in solutions architecture, software engineering, or technical consulting — ideally in an AI, data, or SaaS context
- Strong hands-on track record building and shipping solutions — not just designing them
- Deep fluency with AI-assisted development workflows (Cursor, Copilot, Claude, v0, Replit, etc.) — you build with these tools daily
- Solid understanding of LLM application patterns: RAG, agents, prompt engineering, tool use, and fine-tuning
- Strong engineering judgment — you know when to move fast and when production readiness requires slowing down
- Ability to present your own work clearly to both technical teams and non-technical client stakeholders
- Proven ability to estimate, scope, and defend technical build decisions commercially
- Demonstrated ability to operate autonomously and lead without a fully formed team around you
Nice-to-Haves
- Background in enterprise software, cloud platforms, or AI/ML infrastructure
- Experience in a startup, scale-up, or zero-to-one environment — comfortable building without a playbook
- Familiarity with MLOps, vector databases, or AI safety considerations
- Prior experience influencing a product roadmap from field or client insights
- Exposure to P&L ownership, SOW management, or professional services revenue models
- Background in BPO, CX, or contact center technology
- Experience in regulated industries where responsible AI is non-negotiable
WHAT "AI BUILD FLUENCY" MEANS HERE
This isn't an architecture-on-paper role. We expect our Lead Architect to personally ship working software — not just produce diagrams and hand off to others. You use AI-assisted development tools as a force multiplier, not a crutch. You review AI-generated code critically, make real-time architecture decisions without committee alignment, and have a clear point of view on where these tools accelerate delivery and where they introduce risk.
You should be able to demonstrate all of the following:
- Spin up a working prototype within hours of a client brief
- Critically review AI-generated code — not just prompt and ship
- Make real-time architecture decisions without waiting for committee sign-off
- Scaffold, ship, and iterate on real solutions under commercial pressure
- Coach clients on integrating AI-assisted workflows into their development culture
- Maintain engineering rigor around security, scalability, and maintainability
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