AI Implementation Engineer

Remote – Full-Time
Schedule: 40 Hours per Week | Eastern Time
Exact Hours: Confirmed with the Client
Industry: Physical Therapy & Sports Performance

About the Role

The AI Implementation Engineer, also referred to as an AI Implementation Specialist, is a senior, hands-on technical role responsible for owning, extending, securing, and operating the organization's AI and automation infrastructure.

Unlike an implementation role starting from a blank slate, this position inherits an established technical foundation that already includes:

• A company AI workspace.
• A structured company knowledge base.
• A configured CRM and lead pipeline.
• Project and EOS tracking systems.
• Internal communication and support infrastructure.
• A tested clinical performance-data connector awaiting production deployment.
• Operations manuals.
• Runbooks.
• Access checklists.
• Decision logs.
• A prioritized technical backlog.

The Engineer is accountable for taking ownership of this environment, verifying and strengthening the existing systems, completing unfinished implementations, and building the next generation of AI workflows and automations.

This is a builder's role, not an advisory role.

The Engineer is expected to:

• Write code.
• Configure platforms.
• Integrate APIs.
• Build automations.
• Deploy connectors.
• Monitor production systems.
• Maintain security.
• Document technical infrastructure.
• Train users.
• Own the technical performance of deployed systems.

At the same time, the Engineer must think like an operator: understanding how work actually moves across the organization, identifying opportunities leadership may not have recognized, and returning with functional solutions rather than simply presenting open questions.

The role owns the technical "how" behind the organization's AI, integration, and automation priorities.

Business priorities, EOS operating cadence, and ownership of business SOPs remain with company leadership and the Operations Manager.

Key Responsibilities

Discovery, Handover & System Audit

• Complete a structured handover of the existing AI implementation.
• Work directly with the implementation engineers responsible for building the initial foundation.
• Use existing documentation as the starting point, including:
  • Operations manual.
  • Runbooks.
  • Decision log.
  • Access checklist.
  • Prioritized backlog.
• Verify every existing system directly inside the live environment.
• Identify items documented as unconfirmed.
• Close outstanding technical verification items.
• Keep the operations manual current from the beginning of the engagement.
• Interview key stakeholders across the organization, including:
  • Ownership.
  • Operations leadership.
  • Clinic leadership.
  • Front desk.
  • Virtual administrative team.
• Map current workflows related to:
  • Scheduling.
  • Patient intake.
  • Insurance verification.
  • Lead follow-up.
  • Reporting.
  • Clinical documentation.
• Identify where manual work is consuming unnecessary time.
• Quantify operational inefficiencies where possible.
• Develop a prioritized 90-day automation roadmap.
• Rank automation opportunities based on:
  • Business impact.
  • Ease of adoption.
  • Implementation risk.
• Present the roadmap to ownership for approval.

AI Workspace & Company Knowledge Base

• Administer the organization's Claude Team workspace.
• Manage:
  • User seats.
  • Organization instructions.
  • Projects.
  • Skills.
  • Connectors.
• Maintain the company's Google Drive knowledge base as the single source of truth.
• Ensure one authoritative fact exists in one designated document.
• Version changes appropriately.
• Archive replaced documentation.
• Maintain the canonical index.
• Work with ownership to identify knowledge-base gaps.
• Resolve inconsistencies.
• Move draft documentation toward approved status.
• Build role-specific prompt libraries.
• Build role-specific Claude projects for:
  • Front desk.
  • Clinicians.
  • Performance coaches.
  • Administrative team.
  • Ownership.
• Test every change to organization instructions in a clean session before deploying it to the live environment.

Data, Integrations & Clinical Connectors

• Take the existing VALD performance-testing connector into production.
• Manage deployment of the read-only MCP server.
• Coordinate Microsoft 365 / Entra ID app registration.
• Move credentials into an approved secure vault.
• Deploy the required hosting configuration.
• Conduct sign-in testing.
• Conduct output testing with the appropriate team members.
• Refine AI-generated progress reports using:
  • ForceDecks results.
  • DynaMo results.
• Support reporting designed for:
  • Patients.
  • Clients.
  • Referring physicians.
• Maintain clinical review checkpoints before AI-generated clinical outputs are used.
• Scope future integrations with:
  • EMR systems.
  • Practice-management systems.
  • Additional clinical platforms.
• Ensure integrations remain within approved data guardrails.
• Build and maintain the integration architecture connecting:
  • Claude.
  • CRM.
  • Asana.
  • Slack.
  • Google Workspace.
  • Advertising platforms.
  • Website.
  • Approved clinical systems.

CRM, Lead Capture & Patient Reactivation Automation

• Own the technical configuration of the organization's GoHighLevel CRM.
• Manage:
  • Pipeline stages.
  • Workflows.
  • Calendars.
  • Forms.
  • Users.
  • Phone numbers.
  • A2P messaging registration.
• Ensure every digital lead automatically enters the CRM.
• Connect leads generated through:
  • Paid social.
  • Paid search.
  • Website.
  • Referrals.
• Eliminate unnecessary manual spreadsheets between lead sources and the CRM.
• Automate speed-to-lead processes.
• Automate lead follow-up cadence.
• Ensure leads receive the required number of contact attempts through:
  • Calls.
  • Text messages.
• Track insurance verification through the pipeline.
• Track appointment booking through the pipeline.
• Build patient reactivation campaigns.
• Create:
  • SMS reactivation sequences.
  • Email reactivation sequences.
• Monitor email-domain health.
• Monitor email deliverability.
• Deploy AI assistants within the CRM where appropriate, including potential use cases such as:
  • Lead qualification.
  • After-hours responses.
  • Appointment reminders.
• Maintain clear handoff points from AI automation to a human team member.

Scorecards, Dashboards & Reporting Automation

• Build live, role-specific EOS scorecard dashboards.
• Automatically update dashboards from systems of record.
• Ensure leadership meetings operate using current data.
• Build a marketing ROI dashboard connecting:
  • Advertising spend.
  • Leads.
  • Contact rate.
  • Bookings.
  • Revenue.
• Identify reports currently created manually.
• Automate recurring reporting where appropriate.
• Reduce administrative data-entry requirements for:
  • Operations leadership.
  • Ownership.
• Ensure leadership can focus on decisions rather than manually compiling information.

Content, Marketing & Knowledge-Capture Pipelines

• Build AI-assisted content workflows based on priorities established by ownership.
• Support workflows for:
  • Short-form video editing.
  • Content repurposing.
  • SEO optimization.
  • Blog drafting.
  • Blog publishing.
  • Website SEO improvements.
• Establish meeting-recording workflows.
• Convert appropriate recorded sessions into:
  • Searchable transcripts.
  • SOP drafts.
  • Training materials.
• Support knowledge capture from:
  • Training sessions.
  • Leadership meetings.
  • Process interviews.
• Support the organization's internal training academy through automation.
• Build workflows such as:
  • SOP-update notifications.
  • Course drafts generated from recorded sessions.
• Maintain the distinction that business content ownership remains with the organization.

Testing, Quality Assurance & Deployment

• Test every workflow before production release.
• Test against realistic scenarios.
• Test edge cases.
• Establish human-review checkpoints whenever output is:
  • Patient-facing.
  • Clinically relevant.
• Use version control for:
  • Code.
  • Prompts.
  • Workflow configurations.
• Release changes in small, reversible steps.
• Avoid unnecessary large-scale production changes.
• Establish monitoring for every production workflow.
• Establish alerts for production connectors.
• Verify system behavior after deployment.
• Maintain rollback options where appropriate.

AI Governance, Security & Compliance

• Draft the organization's AI usage and data guardrails for ownership approval.
• Define:
  • Which AI tools may interact with protected health information.
  • Vendor BAA status.
  • Tool-specific usage rules.
  • Team training requirements.
• Route patient-data use cases to ownership until formal policies are approved.
• Maintain secure credential-management practices.
• Store:
  • Passwords.
  • API keys.
  • Tokens. exclusively in an approved password or secrets vault.
• Never place credentials inside:
  • Chat.
  • Tasks.
  • Documents.
  • Email.
• Rotate credentials after account transfers.
• Maintain an appropriate credential-rotation schedule.
• Run the organization's access checklist.
• Verify that the business owns every critical account.
• Remove unnecessary access.
• Conduct quarterly permission reviews.
• Apply least-privilege access principles.
• Keep sensitive data out of the AI knowledge base unless explicitly approved by ownership.
• Ensure logs do not contain patient information.
• Maintain HIPAA-aware technical practices.

Documentation & Team Enablement

• Write a runbook for every production workflow.
• Write a runbook for every production connector.
• Maintain the operations manual.
• Maintain the system architecture map.
• Maintain the decision log.
• Update technical documentation when systems change.
• Train:
  • Owners.
  • Operations leadership.
  • Clinicians.
  • Front desk.
  • Virtual Team Members.
• Create:
  • Short training videos.
  • Quick-reference guides.
• Run the internal support channel for AI and system requests.
• Triage incoming technical requests.
• Resolve appropriate support issues.
• Track requests.
• Document scope changes.
• Maintain visibility into technical priorities.
• Coach AI-trained Virtual Team Members on using deployed workflows and AI tools effectively in their daily responsibilities.

Ongoing Optimization

• Monitor deployed workflows for:
  • Failures.
  • Drift.
  • Cost.
  • Performance issues.
• Address critical failures quickly.
• Continuously improve prompts.
• Continuously improve automation logic.
• Adapt workflows as AI models and platforms evolve.
• Evaluate whether migration to a different platform is justified.
• Recommend migrations when a better solution provides sufficient operational value.
• Identify additional automation opportunities after each implementation.
• Maintain an ongoing improvement backlog.

Budget & Resource Management

• Track AI spending.
• Track API spending.
• Track software spending.
• Track costs:
  • By tool.
  • By workflow.
• Maintain visibility of technology costs for ownership.
• Operate within the approved monthly technology budget.
• Recommend tool consolidation where appropriate.
• Prioritize fewer, better-integrated tools rather than unnecessary platform expansion.
• Identify unused tools.
• Retire unnecessary tools after confirming:
  • Data has been migrated.
  • Workflows have been migrated.
  • Required information has been preserved.

Additional Responsibilities

• Align technical priorities with the Operations Manager once that position is established.
• Work directly with ownership until then.
• Coordinate with external vendors regarding:
  • Integrations.
  • Data access.
  • Technical requirements.
• External partners may include:
  • Billing providers.
  • EMR providers.
  • VALD.
  • Marketing partners.
• Stay current with AI developments relevant to:
  • Healthcare.
  • Physical therapy.
  • Sports performance.
• Contribute useful deployed solutions and lessons learned to Virtrify's internal knowledge base.

Qualifications

Required

• Demonstrated experience building and operating AI workflows and automations in production for a real business.
• Hands-on experience with:
  • Claude and/or OpenAI.
  • API integrations.
  • Prompt engineering.
  • Structured outputs.
  • Connecting AI to business applications.
• Working knowledge of:
  • Python and/or JavaScript/TypeScript.
  • REST APIs.
  • Webhooks.
  • Git.
• Experience administering a CRM and marketing-automation platform.
• GoHighLevel experience strongly preferred.
• Experience with project-management platforms such as Asana.
• Strong security practices, including:
  • Password vaults.
  • Least-privilege access.
  • API key management.
  • Sensitive-data handling.
• Strong operational mindset.
• Ability to map business processes.
• Strong documentation skills.
• Ability to manage multiple initiatives simultaneously.
• Ability to proactively identify opportunities rather than waiting for instructions.
• Professional-level written and spoken English.
• Ability to explain technical concepts clearly to:
  • Business owners.
  • Clinicians.
  • Non-technical team members.
• Bilingual English/Spanish.
• Comfortable solving problems live.
• Ability to walk through real scenarios.
• Ability to explain technical reasoning.
• Ability to adjust solutions under time pressure.

Preferred

• Experience in:
  • Healthcare.
  • Physical therapy.
  • Sports medicine.
  • Wellness.
• Experience handling protected health information in a HIPAA-aware environment.
• Experience building or deploying:
  • MCP servers.
  • Remote connectors.
• Experience with:
  • Microsoft 365.
  • Entra ID app registrations.
• Experience working within an EOS / Traction organization.
• Experience with:
  • Meta Ads.
  • Google Ads.
  • Lead-generation funnels.
  • Marketing attribution.
• Experience with:
  • A2P 10DLC.
  • SMS compliance.
  • Email deliverability.
• Background in:
  • Software engineering.
  • Computer science.
  • Equivalent self-taught technical proficiency.
• Previous:
  • Client-facing experience.
  • Consulting experience.

Core Competencies

The ideal AI Implementation Engineer demonstrates strength in:

• AI Implementation
• AI Workflow Engineering
• Automation Architecture
• API Integration
• CRM Administration
• GoHighLevel
• Claude
• OpenAI
• Prompt Engineering
• MCP Connectors
• Python / JavaScript / TypeScript
• REST APIs
• Webhooks
• Git & Version Control
• Data Integration
• Healthcare Automation
• Security & Access Management
• HIPAA-Aware Systems
• Workflow Monitoring
• Technical Documentation
• Quality Assurance
• Technical Training
• Process Mapping
• Dashboard Automation
• Marketing Automation
• Troubleshooting
• Cost Optimization
• Operational Thinking
• Independent Execution

Typical Workday

This is a full-time role at 40 hours per week, aligned with Eastern Time.

Exact hours will be confirmed with the client.

8:30 AM – Systems & Health Check

• Review overnight workflow runs.
• Review connector health.
• Review system alerts.
• Review the internal support channel.
• Review AI, API, and software spending.

9:00 AM – Priority Sync

• Participate in a short sync with:
  • Ownership, or
  • Operations Manager.
• Review:
  • Current priorities.
  • Blockers.
  • Clinic requirements.
  • Urgent technical issues.

9:30 AM – Deep Work

• Build the current priority workflow.
• Test integrations.
• Refine existing automations.
• Develop technical solutions.
• Iterate based on testing.

12:00 PM – Lunch

1:00 PM – Discovery / Working Session

Meet with an appropriate stakeholder such as:

• Front desk.
• Clinician.
• Performance coach.
• Administrative team member.

The objective may include:

• Mapping a process.
• Understanding an operational issue.
• Testing a workflow.
• Collecting implementation requirements.

2:30 PM – Documentation

Update:

• Runbooks.
• Operations manual.
• Knowledge base.
• Architecture documentation.
• Training materials.

3:30 PM – Deployment, Support or Training

• Deploy tested changes.
• Resolve support tickets.
• Monitor new releases.
• Conduct short training sessions.
• Address implementation issues.

4:30 PM – End-of-Day Update

Provide a concise update covering:

• Progress.
• Completed work.
• Current blockers.
• Next-day priorities.

5:00 PM – End of Day

Typical Workweek

Monday – Planning & Metrics

• Plan the week against the approved roadmap.
• Review the previous week's metrics.
• Refresh scorecard data.
• Prepare current data for the weekly leadership meeting.
• Confirm technical priorities.

Tuesday – Thursday – Build & Ship

Primary focus:

• Build.
• Integrate.
• Test.
• Deploy.
• Document.

These days should maintain a strong emphasis on shipping functional solutions.

Friday – Training, Reporting & Review

• Train the team on newly deployed solutions.
• Deliver the weekly build report.
• Review technology costs.
• Review security items.
• Review outstanding technical risks.
• Plan the following week.

Approximate Distribution of Effort

• 45% – Build, Integration & Deployment
• 15% – Discovery, Workflow Design & Roadmap Planning
• 15% – Monitoring, Optimization & Security
• 15% – Documentation & Team Training
• 10% – Reporting & Stakeholder Communication

Performance Evaluation & Accountability

Final KPIs are confirmed with ownership during the first 30 days.

Initial performance expectations include:

Key Performance Indicators (KPIs)

Handover Completion

Target: Within the first 30 days

• Existing systems verified.
• Access secured.
• Operations manual updated and current.

90-Day Automation Roadmap

Target: Within the first 30 days

• Prioritized automation roadmap completed.
• Roadmap reviewed and approved by ownership.

AI Usage & Data Guardrails

Target: Within the first 30 days

• AI usage and data guardrails drafted.
• Submitted to ownership for approval.

Production Workflows

• Workflows shipped to production according to quarterly targets established with ownership.

Digital Lead Capture

Target: 100%

• 100% of digital lead sources automatically captured in the CRM.
• No manual spreadsheets required between lead source and CRM.

Lead Response Time & Contact Rate

• Tracked from Day 1.
• Demonstrate month-over-month improvement.

Hours Saved

• Establish baseline operational time.
• Measure time savings after automation.
• Report results monthly.

Workflow & Connector Uptime

Target: 98% or Higher

Critical Failure Response Time

Target: Under 2 Hours During Working Hours

Team Adoption

Target: 90% or Higher Active Use

• Monitor adoption of deployed systems and tools.

Runbook Coverage

Target: 100%

• Every production workflow documented.

Credentials & Access

Target: Zero Credentials Outside the Approved Vault

• Complete quarterly access reviews.
• Maintain appropriate permissions.

Tool & API Spend

• Remain within the approved monthly technology budget.

Reporting & Analytics

Weekly Build Report

Submit a weekly report through the team's reporting channel covering:

• Shipped.
• In progress.
• Blocked.
• Next priorities.

Monthly Optimization Report

Report:

• Hours saved.
• Team adoption.
• Workflow failures.
• Tool costs.
• API costs.
• Recommended next automations.

Quarterly Ownership Review

Review:

• Roadmap progress.
• ROI.
• Technical performance.
• Next-quarter priorities.
• Alignment with company quarterly Rocks.

Real-Time Reporting

• Maintain alerts for critical workflow failures.
• Maintain alerts for connector failures.
• Escalate critical technical incidents promptly.

Reporting Structure

The AI Implementation Engineer:

• Reports directly to ownership.
• Works directly with ownership until the Operations Manager is fully established.
• Once the Operations Manager is in place, priorities are established and reviewed with that role.
• Works regularly with:
  • Clinic leadership.
  • Front desk.
  • Performance division.
  • Virtual administrative team.
• Coordinates with Virtrify's implementation team during the initial handover.
• Receives ongoing performance and engagement support through Virtrify's Virtual HR Department™.

Software & Tools

Required Proficiency

• Claude Team
  • Projects.
  • Skills.
  • Connectors.
  • Organization instructions.
• Anthropic API
• OpenAI API
• GoHighLevel
  • CRM.
  • Pipelines.
  • Workflows.
  • Forms.
  • Calendars.
  • SMS.
  • Email.
  • Membership areas.
• Google Workspace
  • Drive.
  • Docs.
  • Sheets.
• Asana
• Slack
• Python and/or JavaScript/TypeScript
• Git
• GitHub
• Password Manager / Secrets Vault

Helpful Experience

• VALD Hub
  • ForceDecks.
  • DynaMo.
  • API.
• Prompt EMR / Practice Management Platform
• Microsoft 365
• Microsoft Entra ID
• Render or Similar Cloud Hosting
• Meta Ads Manager
• Google Ads
• Google Analytics
• Fireflies or Fathom
• Make
• Zapier
• n8n
• SQL
• Basic Data Visualization Tools

Technical Stack Philosophy

Native workflows and direct API integrations should be prioritized whenever practical.

Separate automation platforms such as Make, Zapier, or n8n should be introduced only when the workflow justifies the additional layer of complexity.

The organization prefers fewer well-integrated tools rather than unnecessary platform expansion.

90-Day Onboarding Plan

Month 1 – Handover & Foundation

During the first month, the AI Implementation Engineer will:

• Receive required systems access.
• Complete structured handover with the Virtrify engineers responsible for the initial implementation.
• Review:
  • Operations manual.
  • Runbooks.
  • Decision log.
  • Backlog.
• Verify every existing system.
• Confirm live configurations.
• Secure credentials.
• Move credentials into approved vaults where necessary.
• Interview key stakeholders across the organization.
• Map operational workflows.
• Identify inefficiencies.
• Deliver the 90-day automation roadmap.
• Draft the organization's AI usage and data guardrails.
• Deliver at least one initial quick win.

Month 2 – First Wave Live

During the second month, the Engineer will work toward:

• Deploying the VALD connector once ownership approves applicable data guardrails.
• Verifying CRM lead capture end-to-end.
• Verifying lead follow-up automation end-to-end.
• Eliminating manual gaps between digital lead sources and the CRM.
• Launching the first live EOS scorecard dashboard.
• Ensuring the dashboard is actively used during the weekly leadership meeting.
• Continuing production workflow development.
• Monitoring adoption and reliability.

Month 3 – Shipping Cadence

During the third month, the Engineer will:

• Establish a consistent implementation and deployment cadence.
• Begin content automation pipelines.
• Begin knowledge-capture pipelines.
• Ensure every production workflow has a corresponding runbook.
• Train team members on deployed tools.
• Improve adoption.
• Ensure team members can use deployed tools without constant technical assistance.
• Deliver the first quarterly technical review.
• Review:
  • Roadmap progress.
  • ROI.
  • Workflow performance.
  • Technology costs.
  • Next-quarter priorities.

Virtrify provides ongoing:

• Claude training.
• AI training.
• Model updates.
• Platform updates.
• Support through The Virtual HR Department™.

Work Environment

• Fully remote.
• Full-Time.
• 40 hours per week.
• Working hours aligned with Eastern Time and clinic operating hours.
• Exact hours confirmed with the client.
• Private, dedicated home office with a door.
• Screens must not be visible to other individuals due to potential access to sensitive health information.
• Minimum internet connection:
  • 100 Mbps download.
  • 20 Mbps upload.
• Backup internet connection required.
• Modern computer with:
  • Minimum 16 GB RAM.
  • Current-generation processor.
• Computer must not be shared with other individuals.
• Full-disk encryption required.
• Automatic screen locking required.
• Two-step authentication required on all accounts.
• Dual monitors recommended.
• Professional noise-canceling headset required.
• HD webcam required.
• Power backup or UPS required for stability during outages.
• Quiet, professional background required for video calls.

About the Company

Our client is a growing multi-location physical therapy and sports performance organization operating several outpatient clinics alongside a dedicated performance-training division.

The organization serves a broad range of patients and clients, including:

• Post-surgical rehabilitation patients.
• General physical therapy patients.
• Active individuals.
• Competitive athletes.
• Professional athletes.

The business operates using EOS – the Entrepreneurial Operating System and is led by two clinical owners.

The organization is actively scaling:

• Locations.
• Service lines.
• Team size.
• Marketing.
• Patient acquisition.
• Internal systems.

Over the past several months, the organization has completed a structured AI and systems foundation build.

The existing environment includes:

• Claude Team workspace.
• Company knowledge base within Google Drive.
• GoHighLevel CRM.
• Defined lead pipeline.
• Asana for projects and EOS tracking.
• Slack for communication and support.
• Tested VALD performance-data connector.
• Technical documentation.
• Operations manual.
• Runbooks.
• Decision log.
• Prioritized automation backlog.

The organization is therefore not hiring someone to start its AI transformation from zero.

Instead, the AI Implementation Engineer is expected to take ownership of the existing foundation, complete outstanding implementations, improve security and reliability, increase team adoption, and continuously convert new AI and automation opportunities into working production systems.

Final Notes

The AI Implementation Engineer is expected to become the technical owner of the organization's AI, automation, integration, and supporting systems infrastructure.

Success in this position means:

• The existing AI implementation is fully handed over and verified within the first 30 days.
• System access and credentials are secured.
• The operations manual remains current.
• A prioritized 90-day automation roadmap is approved.
• AI usage and data guardrails are drafted for ownership approval.
• Production workflows are continuously shipped.
• 100% of digital lead sources feed automatically into the CRM.
• Manual spreadsheets are eliminated from lead capture.
• Lead response time and contact rate improve over time.
• Automation creates measurable time savings for ownership and the team.
• Workflow and connector uptime remains at or above 98%.
• Critical failures receive a response within 2 working hours.
• Team adoption of deployed tools remains at or above 90%.
• Every production workflow has a documented runbook.
• No credentials exist outside the approved vault.
• Quarterly access reviews are completed.
• Technology spending remains within the approved budget.
• AI-generated patient-facing or clinical outputs maintain appropriate human-review checkpoints.
• The VALD connector successfully moves into production once approved.
• EOS scorecards increasingly update automatically.
• Leadership spends less time compiling reports manually.
• Employees receive clear training on deployed tools.
• The technical stack remains secure, maintainable, documented, and cost-conscious.
• The Engineer consistently brings forward new automation opportunities rather than waiting to be told exactly what to build.
• Over time, the Engineer develops into the technical leader capable of owning and advancing the organization's AI function as the company continues adding locations, services, and team members.