AI Solutions Architect

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.
• 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.
• Accelerate development, documentation, testing, research, and implementation planning with AI tools.
• 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.

Pay & Benefits

Tampa, FL office in the Water Street district.

• Healthcare plan: medical, dental, and vision.
• 401(k) plan.
• Unlimited PTO.
• Professional development budget.
• Opportunity to help define a core technical function at a growing AI implementation company.
• Direct exposure to production AI deployment across the IT channel.