I lead People and Talent functions at scale — and I build the agentic AI workflows my teams use to do it. Below is one of them, running live.
The short version: I run global talent strategy, and I build the tools my teams use to do it.
Most recruiting leaders manage the funnel. I rebuild it.
Over the last decade I've run global technical talent through hypergrowth, slowdowns, restructures, and PE rolls — owning the workforce planning and forecasting that determined which roles got opened, paused, or eliminated. Along the way I got tired of waiting for engineering bandwidth to fix the tools my teams needed. So I learned to build them myself.
Now I do both jobs. I lead the talent strategy, and I architect the agentic AI workflows — sourcing pipelines, scorecard screeners, interview coordination — that let small talent teams operate like much larger ones. Every workflow is built with a hard human-in-the-loop checkpoint, because the AI is the leverage; the recruiter is still the judgment.
The short version of how I actually think about this job.
A CEO asked me for a hiring metric I couldn't get from the ATS. I built the dashboard that night.
Around the same time, it became clear AI was going to either replace recruiters or amplify them. I picked a side, and I started building the tools that would prove which path was right.
I've been doing both jobs ever since — running TA and writing the code my teams use to run it faster.
Empathy without speed is just nice. The job is both — and that's what the tooling is for.
Submittals and screens are inputs. Quality-of-hire and retention are the only metrics that pay rent.
" I learned to code because waiting on engineering bandwidth meant my recruiters waited too. Fix the bottleneck you control.
" Time-to-fill is a vanity metric if quality-of-hire drops. Track both. Optimize the ratio.
Recommendations from peers, leaders, and team members across the last decade. Pulled verbatim from LinkedIn.
AJ is a rare breed. He is a software developer, engineer, and technologist who applies his experience to the highest talent acquisition level. You can see his impact in how his team responds to his direction — it's probably why his engagement scores far exceed the industry benchmark.
AJ has an innate ability to capture what motivates each of his team members and help them deliver results. He's a natural teacher, and quickly built credibility with the business given his technical background. AJ would be a great people leader for any Technical Recruiting team.
AJ's approach to recruitment is both innovative and impactful. He truly understands the profound responsibility recruiters have in aligning the best talent with companies. His ability to design and execute global hiring — and his guidance on advanced hiring tools — is invaluable.
As Nextiva's Senior Manager of Technical Recruiting, AJ successfully built a top notch team of Technical recruiters in US and Mexico. They achieved incredible results due to AJ's hand-on leadership and coaching. AJ built great relationships with the Development and Engineering Leaders, always surpassing their expectations.
Talent Acquisition is traditionally driven by numbers. The way AJ engages his teams, numbers always follow — and he doesn't have to rely on a spreadsheet. He'll drive to improve any TA function, creating a fast, duplicatable, and highly scalable process.
Occasionally you meet someone who touches your life in a way they probably don't realize. AJ was one of those people — extremely wise and well-versed in the technical world, and an amazing leader. He constantly pushed my team to grow both professionally and personally. Any organization would be lucky to have him.
Three agentic recruiting workflows, each with a hard human checkpoint. Pick one in the console below to watch it run.
Scours target platforms for profiles matching specific requirements, runs semantic mapping, and prepares outreach drafts.
Review candidate match scores & approve outreach templates before dispatch.
Parses resume details, maps background evidence against evaluation rubrics, and highlights core candidate fit markers.
Verify machine justification metrics against raw resume text for final gate approval.
Coordinates calendar openings across panel members, resolves timezone differences, and generates briefing packets.
Authorize schedule proposal blocks before invitations are sent to candidates.
Choose a workflow and run it. Each stage builds up below. The recruiter approves at the checkpoint.
These workflows feed VISION, a live operations dashboard — pipeline health, time-to-fill, source of hire, interviewer load. The same data layer leadership reads to decide what opens, holds, or closes.
Two disciplines I run at the same time. The combination is what most teams need and few people have.
Capacity models and headcount forecasts tied directly to revenue milestones — not wishful org charts.
Fast, honest, respectful pipelines. The kind candidates write about positively even when they don't get the job.
Cost-per-hire, source efficiency, pass-through rates, quality-of-hire post-onboarding. The numbers leadership actually asks for.
Objective rubrics, balanced panels, anonymized screening. Bias gets engineered out — not lectured at.
Multi-stage workflows that source, score, and draft outreach — without the recruiter ever leaving their queue.
Vector embeddings and LLM-based matching surface the right candidates Boolean strings missed for years.
Greenhouse, Lever, Slack, Google Workspace. Custom API and webhook glue so data syncs without manual entry.
Every workflow pauses for recruiter review at every decision gate. AI drafts; humans decide. Non-negotiable.
The roles that built the thesis. Full PDF and LinkedIn for the complete history.
Leading global recruiting through a 139% scale-up while keeping the team's hiring rubric, infrastructure, and tooling in lockstep with the engineering org.
Brought the same workflow design across three PE-backed companies — proving the model isn't company-specific, it's architecture-specific.
Where the thesis got proven. Replaced an external agency spend with an in-house automated sourcing engine and ran the numbers in front of the leadership team.
Took weekly regional office revenue from $10K to $57K in 18 months. Generated $800K in business revenue through pricing automation and 120 active contingent placements.
First TA director seat. Built the daily sourcing and feedback systems that ran the team — outperformed TTF and offer acceptance benchmarks while keeping 100% recruiter retention.
Where I learned to build the tools instead of waiting for them — wrote JavaScript query builders and scraping utilities for the team. Generated $1.4M in gross profit. President's Club + Boolean Sourcing Competition honors.
What recruiters and hiring leaders most often ask about this work.
Standard automation runs a fixed script. An agentic workflow can reason, branch, retry, and call its own tools — like a recruiter who knows when to broaden a search, flag low-confidence data, or stop and ask for help.
I build them with a hard rule: the agent does the work, but the recruiter makes the decisions. Every workflow pauses at a human checkpoint before anything reaches a candidate.
In my experience, yes — for the right roles. Semantic search finds people whose experience maps to a role even when their resume doesn't use the right keywords. Boolean misses them.
The bigger gain isn't precision, though. It's that the agent drafts personalized outreach for every candidate it surfaces, which is what actually moves response rates. A 10-candidate list with real hooks beats a 100-candidate list with templates.
Three things: I redact identifiers (name, photo, address, school) before scoring. I require cited evidence for every rubric score — no opaque outputs. And the agent never makes a final call; it's an advisor, not a decision-maker.
The bias guardrails aren't a nice-to-have. With laws like NYC Local Law 144 and the EU AI Act, they're load-bearing.
If you need an engineer to own and ship a production system, I'm the wrong hire. My technical fluency exists to direct the build, not to be the build.
I'm the right hire when you need a strategic TA leader at the top of the function — someone who can architect agentic workflows, set the rubrics, and brief the engineers — while staying focused on the human side of recruiting that AI can't replace.
Open to senior Talent Strategy leadership roles, advisory work, and conversations about workflow design.
Based in Dallas, TX. Remote, hybrid, or relocation considered for the right role.