AI Implementation Specialist

Remote – Full-Time Schedule: Monday – Friday | 8:30 AM – 5:00 PM

About the Role

The AI Implementation Specialist is a senior technical contributor embedded directly within the executive team, working closely with the founder to transform business ideas and operational challenges into working AI-powered systems.

This is not an IT support role and not a back-office administrative position. It is a hands-on technical role responsible for designing, building, testing, deploying, and maintaining AI workflows and automations that eliminate operational bottlenecks across the organization.

The Specialist will translate business problems into technical solutions and take those solutions all the way into production.

The initial priority will be developing an AI-driven executive-support layer that captures sales follow-ups, identifies commitments, surfaces what requires attention, and prevents important opportunities or responsibilities from falling through the cracks. The role will then expand into data analysis, automated reporting, leadership dashboards, and a growing library of AI agents that automate repetitive, high-value work throughout the organization.

This professional will participate directly in the leadership rhythm of the business, including weekly leadership meetings, and is expected to develop a strong understanding of the entire operation in order to identify where AI and automation can create the greatest business leverage.

Why This Role Is a Career-Defining Opportunity

This position offers a technically strong professional the opportunity to have direct ownership, direct access to leadership, and immediate visibility into the business impact of their work.

Rather than operating within a traditional IT department or responding to tickets, the AI Implementation Specialist will work directly with the founder and leadership team to determine which problems should be solved first based on business impact.

The position also provides a clear potential growth trajectory:

AI Implementation Specialist → AI Operations Coordinator → Operations or Innovation Leadership

As the organization's AI footprint expands, the professional in this position will have the opportunity to build, own, and eventually lead a broader automation and AI roadmap.

Key Responsibilities

Executive Automation Layer – First Priority

• Design and deploy an AI-driven executive-support system.

• Monitor the founder's email and relevant communication channels through approved company systems.

• Identify open follow-ups and commitments.

• Surface priorities requiring leadership attention each day.

• Build automated daily briefings summarizing prior-day activity.

• Identify commitments made during communications and meetings.

• Recommend appropriate next actions.

• Connect the CRM with the automation layer.

• Automatically capture and update:

  • Opportunities
  • Contacts
  • Follow-ups

• Reduce manual CRM updates.

• Build meeting-intelligence workflows that review recorded calls.

• Identify records that were not properly updated following meetings.

• Prepare suggested updates for one-click approval.

• Build systems designed to prevent sales commitments, leads, and follow-ups from falling through the cracks.

Data Analysis & Reporting

• Build and automate product-velocity analysis across a multi-tier distribution chain.

• Analyze performance from:

  • Manufacturer
  • Distributor
  • Regional operator
  • End user

• Track performance by:

  • Product
  • Flavor
  • Channel
  • End customer

• Identify what is growing.

• Identify products or channels that are stalling.

• Surface areas at risk.

• Consolidate information from spreadsheets, distributor feeds, and internal systems.

• Transform fragmented data into clean and reliable automated reporting.

• Proactively identify anomalies and trends.

• Route alerts to the appropriate business owner.

• Replace legacy manual spreadsheet processes with automated pipelines wherever practical.

AI Workflow Design

• Architect end-to-end AI workflows combining:

  • Large Language Models (LLMs)
  • Automation platforms
  • Existing company software

• Select the appropriate AI model for each use case.

• Balance model cost against required capabilities.

• Design robust prompting and structured-output strategies.

• Implement retrieval-augmented generation (RAG) where appropriate.

• Design appropriate reasoning and workflow logic for complex tasks.

• Plan for:

  • Error handling
  • Edge cases
  • Human-in-the-loop checkpoints

• Document architecture decisions.

• Document technical tradeoffs.

Build & Deployment

• Build automations using modern automation platforms.

• Develop custom integrations when necessary.

• Integrate AI workflows with:

  • CRM systems
  • Project-management platforms
  • Communication platforms
  • E-commerce systems
  • Data systems

• Write supporting Python and/or JavaScript scripts when no-code or low-code platforms are insufficient.

• Configure data stores.

• Develop retrieval workflows when required by the use case.

• Test workflows before production release.

• Deploy approved workflows into production.

• Take ownership of the technical performance of deployed systems.

Dashboards & AI Agents

• Build leadership dashboards that make business performance visible at a glance.

• Design AI agents for recurring, rules-based operational work.

• Deploy agents capable of owning appropriate workflows end-to-end.

• Prioritize agent development according to business impact.

• Maintain a prioritized backlog of automation opportunities.

• Connect automation priorities to measurable outcomes such as:

  • Revenue generated
  • Hours saved
  • Costs reduced

Testing & Quality Assurance

• Test every workflow against real production scenarios before deployment.
• Develop test cases for edge conditions.
• Identify and test potential failure modes.
• Validate AI output quality through structured evaluation.
• Implement monitoring for deployed workflows.
• Configure alerts for failures.
• Track workflow failure rates.
• Intervene before individual failures become larger operational problems.

Documentation & Handoff

• Create operational runbooks for every deployed workflow.

• Develop training materials for team members who will use the systems.

• Document:

  • Integrations
  • Environment variables
  • Credential handling
  • System dependencies

• Maintain an architecture overview of the organization's AI technology stack.

• Conduct training sessions for the broader team.

• Ensure deployed solutions can be understood, maintained, and appropriately operated beyond the original developer.

Ongoing Optimization

• Monitor deployed workflows for performance degradation.
• Monitor systems for model or workflow drift.
• Continuously improve prompts and logic.
• Adapt workflows as business requirements change.
• Identify new automation opportunities revealed by previous deployments.
• Maintain version control for production workflows.
• Manage model updates.
• Manage dependency upgrades.

Security & Compliance Awareness

This position will have significant access to company systems, executive communications, operational information, financial data, and credentials. Maintaining that trust is a core responsibility.

The AI Implementation Specialist must:

• Handle confidential business, financial, and operational information with discretion.
• Securely manage API keys and credentials.
• Follow least-privilege access principles.
• Respect established data boundaries.
• Work within company-owned accounts and systems rather than personal accounts.
• Ensure access can be properly granted and revoked.
• Proactively raise security or data-handling concerns instead of creating workarounds that introduce unnecessary risk.

Reporting & Analytics

The AI Implementation Specialist will provide:

• Weekly Automation Performance Summary

  • Workflows currently running
  • Hours saved
  • Costs saved
  • Failures identified and resolved

• Monthly Optimization Report

  • Improvements deployed
  • New opportunities identified

• Quarterly Strategic Review

  • Automation roadmap progress
  • Return on investment
  • Strategic recommendations

• Real-Time Alerts

  • Critical workflow failures requiring immediate attention

Additional Responsibilities

• Participate in weekly leadership meetings.
• Operate within the organization's established meeting cadence.
• Coordinate closely with leadership and department owners.
• Understand operational reality rather than relying exclusively on documented processes.
• Mentor team members on practical AI usage and best practices.
• Contribute to the company's shared knowledge base.
• Document lessons learned from deployed solutions.
• Stay current with relevant developments in AI capabilities and platforms.

Qualifications

Background

• Demonstrated experience building AI workflows and automations in production environments.
• Engineering, Computer Science, or equivalent technical foundation.
• Formal education is welcomed but equivalent self-taught technical experience may qualify.
• Demonstrated track record of shipping working systems rather than only prototypes.

How You Think

• Able to break business problems into systems.
• Focused on creating leverage rather than simply completing isolated tasks.
• Prioritizes work according to measurable business impact.
• Understands the importance of addressing high-value opportunities first.
• Anticipates downstream consequences.
• Designs systems with scalability in mind.

How You Work

• Hands-on and self-directed.

• Takes initiative without requiring repeated direction.

• Comfortable taking responsibility for the technical performance of deployed solutions.

• Disciplined regarding:

  • Testing
  • Documentation
  • Version control

• Strong interest in understanding how the business operates and why each technical project matters.

Communication

• Bilingual English and Spanish, written and verbal.
• Able to explain technical concepts clearly to non-technical stakeholders.
• Professional and polished when working directly with leadership.
• Strong follow-through.
• Consistently closes communication and execution loops.

Technical Stack Expectations

Candidates should have practical experience with relevant technologies across the AI implementation stack, including:

• Large Language Model platforms and APIs:

  • OpenAI
  • Anthropic

• Automation platforms such as:

  • n8n
  • Make
  • Zapier
  • Lindy
  • Relevance AI

• Python and/or JavaScript.

• Version control.

• Structured change management.

• Data stores.

• Retrieval-Augmented Generation (RAG), when appropriate.

• CRM integrations.

• Project-management integrations.

• Communication-platform integrations.

• E-commerce integrations.

Candidates should be hands-on with at least two of the following:

• OpenAI API
• Anthropic API
• n8n
• Make
• Zapier
• Lindy
• Relevance AI

Additional expectations include:

• Working Python or JavaScript knowledge for custom integrations.
• Strong prompt-engineering capabilities.
• Structured-output experience.
• Experience with at least one CRM.
• Experience with at least one project-management platform.

Preferred experience includes:

• Retrieval-Augmented Generation.
• Data stores.
• Client-facing or consulting work.
• Food manufacturing.
• Distribution.
• Comparable multi-channel operations.

Typical Workday

8:30 AM – System & Workflow Review

• Review overnight workflow performance.
• Resolve identified failures.
• Check automation dashboards.
• Review spend and system-performance information.

9:00 AM – Daily Stand-Up

• Meet with the operations lead and Virtrify operations support.
• Review priorities, blockers, and active projects.

9:30 AM – Deep Work

• Build active workflows.
• Test automations.
• Iterate on systems currently in development.

12:00 PM – Lunch

1:00 PM – Stakeholder / Discovery Session

• Meet with stakeholders.
• Understand business processes.
• Identify new automation opportunities.
• Gather technical and operational requirements.

2:30 PM – Documentation

• Update runbooks.
• Document systems.
• Prepare training materials.

3:30 PM – Deployment & Knowledge Transfer

• Conduct code reviews.
• Deploy tested workflows.
• Transfer knowledge to operating teams.

4:30 PM – End-of-Day Reporting

• Summarize progress.
• Identify blockers.
• Establish next-day priorities.

5:00 PM – End of Day

Typical Workweek

• Monday: Weekly planning, backlog prioritization, and prior-week performance review.
• Tuesday – Thursday: Build, test, deploy, and document, with heavy emphasis on shipping working systems.
• Friday: Discovery, team training, weekly performance-dashboard delivery, and optimization review.

Approximate Effort Distribution

• 50% – Build and deployment
• 20% – Discovery and workflow design
• 15% – Documentation and training
• 10% – Optimization and maintenance
• 5% – Reporting and stakeholder communication

Performance Evaluation & Accountability

The AI Implementation Specialist is accountable for both technical execution and measurable business impact.

Performance will be evaluated based on the ability to consistently design, ship, maintain, document, and improve production-ready AI systems while demonstrating strong business judgment and ownership.

Key Performance Indicators (KPIs)

• Production Deployment: Workflows shipped to production each quarter against the target established for the engagement.
• Hours Saved: Measurable hours saved before versus after deployment.
• Cost Reduction: Cost savings directly attributable to deployed automations.
• Workflow Uptime: Target of 98% or higher.
• Critical Failure Response: Target response time of under 2 hours.
• Tool Adoption: Target of 90%+ active use among intended operating-team users.
• Documentation Completeness: Deployed systems are accompanied by appropriate technical and operational documentation.
• Leadership Satisfaction: Evaluated during quarterly reviews.

Reporting Structure

• Reports directly to the Founder or designated Technical/Operations Leader.
• Coordinates daily with the operating team.
• Participates directly in leadership and stakeholder workflows.
• Performance and engagement health are reviewed weekly through Virtrify's Virtual HR Department.

Software & Tools

• HubSpot – CRM
• Monday.com – Project management
• Slack – Communication
• Google Workspace – Productivity and email
• Shopify – E-commerce
• Anthropic / Claude – AI platform
• OpenAI – AI platform and APIs
• n8n / Make / Zapier / Lindy / Relevance AI – Automation platforms
• Python and/or JavaScript – Custom scripting and integrations
• Version Control & Developer Tooling – Production workflow management

4-Week Training & Onboarding Plan

Week 1 – Systems & Business Orientation

• Receive access to company systems.
• Review the existing technology stack.
• Review the current automation inventory.
• Learn the company's business model.
• Understand its different sales and distribution channels.

Week 2 – Stakeholder Discovery

• Conduct stakeholder sessions across departments.
• Understand operational pain points.
• Identify existing bottlenecks.
• Understand departmental priorities.
• Begin identifying high-impact automation opportunities.

Week 3 – First Workflow Build & Deployment

• Build the first production workflow under review.
• Begin with the executive automation layer.
• Test the workflow.
• Incorporate feedback.
• Deploy the approved solution.

Week 4 – Independent Ownership

• Transition into independent workflow ownership.
• Manage active automation projects.
• Participate in weekly review checkpoints.
• Begin independently identifying and prioritizing additional opportunities.

Following the initial four-week onboarding period, Virtrify provides ongoing technical training through The Virtual HR Department™, including model updates, platform releases, new-technique training, and access to a peer community of AI Implementation Specialists for sharing strategies and implementation practices.

Work Environment

• Fully remote position.

• Dedicated home office with a door for privacy during stakeholder and leadership calls.

• Minimum internet connection:

  • 100 Mbps download
  • 20 Mbps upload

• Backup internet option such as a mobile hotspot or secondary connection.

• Modern computer with:

  • Minimum 16 GB RAM
  • Current-generation processor

• Dual monitors recommended.

• Professional noise-canceling headset.

• HD webcam.

• Power backup or UPS for continuity during outages.

• Quiet and professional background for video meetings.

About the Company

Our client is a well-established, fast-growing specialty food manufacturer and foodservice company operating across the United States.

The organization produces and distributes products at scale across:

• High-volume venues
• Wholesale partners
• National distributors
• Stadiums
• Convention centers
• Cultural institutions
• Airlines
• Nationally recognized events

The organization operates through multiple entities with internal teams covering:

• Production
• Logistics
• Finance
• Sales
• Administration

The business is simultaneously scaling across multiple sales channels and transitioning from manual, spreadsheet-driven processes toward AI-augmented operational systems.

Leadership has already begun developing internal tools and adopting AI. The next stage is establishing dedicated technical ownership so these systems can be properly designed, built, deployed, and maintained rather than developed incrementally alongside unrelated responsibilities.

The organization's culture emphasizes accountability, sound judgment, initiative, strong follow-through, business understanding, and genuine ownership.

Final Notes

The AI Implementation Specialist is expected to become the organization's technical owner for practical AI implementation, working directly with leadership to transform ideas and operational challenges into production-ready systems.

Success in this position means:

• The founder gains an effective AI-driven executive-support layer.
• Sales commitments and follow-ups stop falling through the cracks.
• Manual CRM activity is increasingly automated.
• Leadership receives useful daily intelligence rather than additional raw information.
• Fragmented spreadsheets and data sources become reliable automated pipelines.
• Leadership gains clear visibility through dashboards and automated reporting.
• High-value repetitive work is progressively handled through well-designed AI agents.
• Automations are tested, monitored, documented, and maintained rather than simply launched.
• AI initiatives produce measurable improvements in hours saved, costs reduced, operational reliability, or revenue opportunity.
• Team members understand and adopt the systems that are deployed.
• The Specialist develops a deep enough understanding of the business to proactively identify where automation will create the greatest leverage.
• Over time, the position can evolve from individual AI implementation into broader AI operations and innovation leadership.