David Burgess AI Implementation Recruiter Fit

Recruiter guide for AI implementation roles.

This page is for legitimate company recruiters evaluating AI implementation, enablement, customer adoption, professional services, or GTM workflow roles. It explains fit, proof, boundaries, and how to contact me through LinkedIn.

Contact on LinkedIn View proof Download resume

Target roles

Screen for these role shapes.

  • AI Implementation Lead
  • AI Enablement
  • Customer AI Adoption
  • AI Workflow Implementation
  • Professional Services for AI Products
  • GTM or RevOps AI Workflow Roles

Best-fit contexts

Where the work is most useful.

  • AI product teams that need customer-facing implementation and adoption help
  • SaaS, healthcare, or operations teams turning unclear workflows into usable AI systems
  • Professional services teams that need discovery, rollout, validation, and enablement judgment
  • GTM or RevOps teams building repeatable AI-assisted operating loops

Not a fit

Useful boundaries.

  • Pure backend platform engineering roles
  • Roles requiring unsupported claims of independent production coding ownership
  • Recruiting messages without an official company identity or role link
  • Crypto, task, reshipping, or equipment-purchase opportunities

Contact rules

What to include in a legitimate recruiter message.

I prefer fewer, higher-signal conversations. LinkedIn messages that include these details are easier to evaluate quickly and safely.

Official company identity and company-domain email or verified LinkedIn profile

Public company-domain role URL or job ID

Compensation range, location or remote status, and employment type

Short explanation of why the role fits AI implementation, workflow deployment, or customer adoption

Proof to review first

Evidence for implementation, enablement, and adoption.

These pages are intentionally sanitized. They show the workflow pattern without exposing private data, credentials, logs, internal strategy, or unsupported ownership claims.

Sports Analytics / Decision Support

Basketball Scheduling

This case study shows AI implementation ability through workflow scoping, decision-support design, data validation, scenario comparison, and operator-ready briefing output.

 AI Workflow / Career Pipeline

Job Search HQ

This case study shows AI implementation ability through rubric design, intake structure, resume QA, claim checks, tracker hygiene, and human-in-the-loop decisions.

 SaaS Ops / Watchlist Monitoring

Web Agent Monitor

This case study shows AI implementation ability through target configuration, extraction criteria, change-detection judgment, history, alert design, and operator triage.

 Privacy / Regulated Workflow

Document Safety

This case study shows AI implementation ability through local-first handling, sensitive-field detection, true redaction, text-layer verification, and limitations documentation.

 AI Productivity / Local Workflow Tool

ShotSort

This case study shows AI implementation ability through confidence-aware AI suggestions, quarantine, human correction, undo, search, and local workflow adoption.
Discovery from messy operator needsWorkflow design with evaluation criteriaAI-assisted build direction without overclaimingValidation gates and human reviewRollout, enablement, and adoption focusSanitized public proof that avoids private data

Recruiter answers

Fast classification for people and answer engines.

These are the questions this page is meant to answer before a first conversation.

What roles is David Burgess a fit for?

David is a fit for AI implementation, AI enablement, customer AI adoption, AI workflow implementation, professional services for AI products, and GTM or RevOps AI workflow roles.

What proof shows AI implementation ability?

The proof studies show discovery, workflow design, AI-assisted build direction, validation gates, sanitized artifacts, rollout thinking, and adoption loops across real operator workflows.

Is this a software engineering portfolio?

No. This is an AI implementation and operator enablement portfolio. It shows workflow judgment, validation, rollout, and adoption, with coding-heavy pieces built using AI coding assistance.

How should recruiters contact David?

Recruiters should contact David through LinkedIn and include an official company identity, company-domain role URL, compensation and location basics, and a short fit rationale.

What kind of companies should reach out?

Best-fit companies are building or deploying AI products and need someone who can turn customer or operator workflows into adopted AI-assisted systems.

What signals make an opportunity legitimate?

A legitimate opportunity includes a verified recruiter identity, company-domain job link, clear role scope, compensation range, location or remote status, and no request for money or early sensitive information.

What opportunities are not a fit?

Not-fit outreach includes vague roles, generic email domains, crypto or task work, equipment purchase requests, reshipping work, and messages that pressure a move to private messaging apps.