AI Solutions Architect

PRESHai is hiring an AI Solutions Architect to lead the technical architecture and integration readiness of PRESHos client implementations.

This role is responsible for turning client business needs into practical AI system designs that can be built, tested, deployed, and adopted inside real client environments. You will work across discovery, solution design, rapid prototyping, implementation planning, and technical handoff.

The right person for this role is not a traditional enterprise architect and not simply a senior engineer. This is a hands‑on architecture role for someone who can understand a client’s business problem, evaluate their existing technology stack, design the right AI‑enabled system, guide the build, and ensure the final deliverable is ready for integration.

You will work across client environments that may include Microsoft 365, Azure, AWS, GCP, Salesforce, ServiceNow, HubSpot, ConnectWise, Autotask, Clio, ERP platforms, communication tools, databases, APIs, and custom internal systems.

You will also serve as a senior technical voice in client conversations, supporting discovery, scoping, demos, implementation planning, and technical knowledge transfer.

Responsibilities

Client Discovery & Solution Architecture

• Lead technical discovery for PRESHos and AI implementation opportunities.
• Translate client goals, workflows, pain points, and system constraints into clear technical architecture.
• Design how PRESHos fits into a client’s current technology stack, permissions, systems of record, data flows, and operational processes.
• Identify dependencies, risks, integration requirements, and implementation paths before work begins.
• Support proposals, demos, statements of work, and solution narratives with credible technical direction.
• Help determine what should be built, what should be validated first, and what should not be built.

AI System Design

• Design AI-enabled systems across agents, tools, integrations, workflow orchestration, RAG, voice interfaces, observability, and governance.
• Define tool‑calling patterns, prompt architecture, data access patterns, error handling, escalation paths, and human‑in‑the‑loop workflows.
• Build reference architectures and reusable patterns that improve speed and consistency across client implementations.
• Make technology recommendations based on client fit, implementation speed, security, maintainability, and integration requirements.
• Stay current on AI‑native development tools, LLM APIs, agent frameworks, MCP tooling, workflow systems, and developer infrastructure.

Integration Readiness

• Ensure client‑facing technical work is ready for real implementation, not just a working demo.
• Prepare architecture documentation, handoff notes, dependency documentation, and implementation guidance.
• Confirm that configuration, environment variables, credential handling, and deployment requirements are clearly defined.
• Review core logic, integration points, source code quality, known limitations, and support requirements before client handoff.
• Ensure deliverables are understandable, testable, configurable, secure, and practical for client technical teams to adopt.

Technical Leadership

• Guide Forward Deployed Engineers across active client workstreams.
• Set technical direction and help engineers make practical architecture and integration decisions.
• Review code and technical approaches for quality, maintainability, and client fit.
• Pair with engineers on complex architecture, integration, and AI system design problems.
• Help unblock implementation work quickly without sacrificing long‑term maintainability.
• Build reusable internal delivery patterns, technical documentation, and implementation playbooks.

Client Implementation Support

• Work directly with client engineering, IT, operations, and business stakeholders during implementation.
• Support integration planning without requiring direct access to client production credentials.
• Help resolve integration questions, clarify architecture decisions, and support knowledge transfer.
• Participate in sprint planning, demos, feedback sessions, and executive technical reviews as needed.
• Represent PRESHai’s technical approach clearly and confidently in client‑facing conversations.

AI‑Native Delivery

• Use AI‑native development tools as part of the daily delivery workflow.
• Work comfortably with tools such as Claude Code, Cursor, Codex, LLM APIs, AI‑assisted testing, and AI‑assisted documentation.
• Use AI tools to accelerate development, documentation, testing, research, and implementation planning.
• Evaluate emerging tools and turn effective approaches into repeatable PRESHai delivery patterns.

Required Qualifications

• 5+ years of experience designing, building, and shipping production software systems.
• Hands‑on experience building LLM‑powered, AI‑enabled, automation, integration, or workflow systems in production environments.
• Strong production coding ability in TypeScript and/or Python.
• Practical experience with APIs, databases, OAuth flows, third‑party integrations, webhooks, and system‑to‑system data movement.
• Experience with LLM application patterns, including tool/function calling, RAG, prompt architecture, structured outputs, evals, and workflow automation.
• Strong integration experience across systems you did not choose, such as CRM, PSA, ITSM, ERP, communication platforms, databases, and custom internal systems.
• Fluency in at least one major cloud ecosystem, such as Azure and Microsoft 365, AWS, or GCP.
• Experience with PostgreSQL, API design, event‑driven systems, durable workflow patterns, and production observability.
• Ability to lead technical discovery with senior client stakeholders.
• Ability to review code, set technical direction, and guide other engineers.
• Strong written and verbal communication skills.
• Comfort operating in a fast‑moving environment with multiple active client workstreams.

Preferred Qualifications

• Experience building agentic AI systems, tool‑calling systems, MCP servers, or multi‑step AI workflows.
• Experience implementing AI, automation, or integration systems in MSP, VAR, distributor, vendor, professional services, legal, or complex B2B environments.
• Microsoft 365 and Azure experience, including Graph API, Teams, Entra ID, and Bot Service.
• Production voice AI experience using Twilio, ElevenLabs, OpenAI Realtime API, or similar platforms.
• Experience with MCP servers and tool‑based agent architecture.
• Experience with Inngest, Temporal, or comparable workflow orchestration platforms.
• Experience in consulting, agency, implementation, or professional services environments.
• Experience designing systems that other engineers had to build, operate, or integrate.
• Experience working directly with client technical teams during implementation or handoff.

What Success Looks Like

• PRESHos implementations have clear technical architecture before work begins.
• Client stakeholders understand how AI systems will fit into their existing workflows, data, permissions, and technology environments.
• Forward Deployed Engineers move faster because your architecture, patterns, reviews, and direction remove ambiguity.
• Client‑facing deliverables are well documented, secure, testable, configurable, and ready for integration.
• Client engineering teams trust PRESHai’s technical approach.
• Reusable architecture patterns compound across client engagements.
• PRESHai wins more work because our AI implementation capability is practical, credible, and clearly communicated.

Compensation & Benefits

• Tampa, FL office in the Water Street district.
• Healthcare plan: medical, dental, and vision.
• 401(k) plan.
• Unlimited PTO.
• Professional development budget.
• Travel approximately 10% to 15% for client engagements and on‑site sessions.
• Opportunity to help define a core technical function at a growing AI implementation company.
• Direct exposure to production AI deployment across the IT channel.