The AI Implementation Engineer serves as the CEO’s operational right hand, responsible for building AI-powered systems across sales, marketing, and operations.

This is a high-impact, cross-functional role focused on:

• Systemizing revenue operations
• Building accountability frameworks
• Automating workflows using AI
• Creating scalable infrastructure

The role requires engineering thinking (systems, processes, optimization) rather than traditional coding.

Key Responsibilities

Discovery & Workflow Mapping

• Analyze and document all business workflows across departments
• Conduct structured interviews with leadership and teams
• Map full revenue cycle (lead → nurture → conversion → funding)
• Identify inefficiencies and bottlenecks
• Build AI implementation roadmap based on ROI and impact
• Document and standardize SOPs

Sales System Development & Documentation

• Document and systematize CEO’s sales methodology
• Build AI-powered sales playbooks (Claude-based)
• Create training and onboarding systems for sales reps
• Develop role-play and objection-handling tools
• Standardize sales processes for scalability

Sales Team Accountability Infrastructure

• Implement sales KPIs (calls, emails, meetings, pipeline movement)
• Build real-time dashboards for performance tracking
• Create automated alerts for underperformance
• Establish structured weekly sales check-ins
• Provide data-driven performance insights

Conference & Pipeline Optimization

• Redesign conference-to-revenue workflow
• Build pre-event, during-event, and post-event processes
• Implement AI-powered follow-up sequences
• Design LinkedIn outreach strategies
• Track ROI per conference (leads, calls, revenue)
• Build repeatable, scalable templates

Marketing & Relationship Systems

• Develop LinkedIn strategy (CEO + company page)
• Build attorney relationship tracking system
• Structure referral tracking and engagement
• Create marketing content strategy
• Manage offshore engagement team workflows

Service Delivery Optimization

• Identify bottlenecks in intake-to-funding pipeline
• Implement automation for follow-ups and tracking
• Support document processing and compliance workflows
• Build dashboards for operational KPIs
• Improve turnaround times and pipeline visibility

AI Infrastructure & System Ownership

• Implement Claude as core AI operating system
• Build AI workflows, agents, and prompt libraries
• Integrate AI tools across departments
• Monitor usage, cost, and performance
• Maintain centralized documentation and knowledge base

CEO & Leadership Support

• Support CEO with AI-powered workflows (email, meetings, decision-making)
• Deliver weekly operational reports (sales, marketing, pipeline)
• Assist with hiring planning and organizational design
• Provide execution support on strategic initiatives

Qualifications

Background

• Engineering or analytical background (mechanical, industrial, civil, etc.)
• Completion of AI Implementation Engineer training (Virtrify Academy or similar)

Core Competencies

• Strong systems thinking and problem-solving ability
• Ability to break down and optimize complex workflows
• High ownership and self-direction
• Data-driven and performance-oriented mindset
• Ability to operate in ambiguity

Communication & Work Style

• Strong written and verbal English communication
• Bilingual English/Spanish preferred
• Ability to explain systems in simple terms
• Comfortable leading training sessions
• High accountability and proactive mindset

Typical Work Day

• Review pipeline activity and sales performance
• Check dashboards and identify issues
• Align with CEO on priorities
• Build and optimize AI workflows
• Support sales and marketing execution
• Coordinate with operations and offshore team
• Execute outreach and automation tasks
• Provide end-of-day updates

Typical Workweek

• Monday: Sales and marketing review, CEO alignment
• Tuesday: Sales accountability and pipeline optimization
• Wednesday: Marketing execution and conference workflows
• Thursday: Deep work (AI systems, SOPs, automation)
• Friday: Reporting, analysis, and planning

Performance Evaluation

Performance is measured based on:

• Sales system implementation and adoption
• Dashboard and KPI visibility
• Conference pipeline conversion rates
• LinkedIn outreach performance
• Reduction in CEO workload
• AI system deployment across functions
• SOP documentation completeness
• ROI generated from implementations

Key Performance Indicators (KPIs)

• Sales playbook deployed within 30 days
• Sales dashboards live within 21 days
• 95%+ post-conference follow-up completion
• LinkedIn outreach generating conversations within 45 days
• 50%+ reduction in CEO time on sales management (within 90 days)
• AI systems implemented across at least 3 core functions
• Full revenue workflows documented within 90 days

Work Environment

• Fully remote role
• Fast-paced, high-performance environment
• Requires high autonomy and ownership
• Must handle cross-functional responsibilities
• Alignment with U.S. Eastern Time

Software & Tools

• Claude (primary AI system)
• ChatGPT / Gemini
• LinkedIn and social platforms
• Email marketing tools
• CRM systems (Salesforce or similar)
• Automation tools
• Project management tools
• Communication tools (Slack, Zoom, email)

Final Notes

This role is one of the most impactful positions in modern business operations. The AI Implementation Engineer directly transforms how the company operates by building systems that drive efficiency, scalability, and revenue growth.

Success in this role means:

• Fully systemized sales and marketing operations
• Strong accountability infrastructure
• AI-driven workflows across the business
• Reduced dependency on the CEO
• Scalable growth systems

About our Client

A well-established pre-settlement funding company operating across 37 U.S. states, providing financial solutions to the personal injury legal market.

The company offers:

Non-recourse cash advances to plaintiffs awaiting settlements Case expense financing for law firms

With over 16 years in operation and projected annual revenue of ~$10M, the company has strong service delivery operations but lacks structured systems on the revenue side (sales, marketing, and growth infrastructure).

The CEO currently manages multiple growth functions without a unified system, creating an opportunity to implement scalable, AI-driven infrastructure.