Applied AI
Engineering
for Real‑World Impact
Independent AI consultant. Previously built production voice AI infrastructure at scale—now helping teams ship systems that actually work. Based in Portland, OR — working globally.
What I Do
Production-grade AI engineering for teams that need to ship, not just prototype.
Voice AI & ASR
Speech pipeline architecture, API integration, and production deployment of voice systems that hold up under real-world load.
LLM Evaluation
Eval frameworks that surface the failure modes that matter — not just benchmark scores.
ML Infrastructure
Data pipelines, MLflow/Prefect workflows, and internal tooling built for long-term maintainability.
AI Training & Workshops
Facilitated training for engineering and product teams — in-person or remote, with reusable materials.
Developer Relations
Technical content, API documentation, and developer advocacy that helps engineers actually understand your product.
About Li
I'm Elliot "Li" Bearden, an independent AI consultant previously working as an Applied AI Engineer at Deepgram (now a $1.3B voice AI platform serving 200,000+ developers and 1,300+ enterprise customers, with over a trillion words transcribed). I joined early, started in customer support, and worked my way into data pipelines, model evaluation, and custom training programs before going independent.
That trajectory taught me something most consultants miss: the hardest problems aren't in the model—they're in the gap between a working demo and a system people actually trust. I help teams close that gap. Whether that's eval frameworks that surface real failure modes, pipelines that hold up under production load, or training that sticks with the humans in the loop.
Outside of AI, I'm a conservation enthusiast and outdoorsman. I build tools that connect underserved communities with the natural world, and I think a lot about what it means to build technology that serves people rather than just capturing their attention.
Core Expertise
Responsible AI
I don't treat ethics as a compliance layer you add at the end. It's part of how I think about every engagement from the start.
Evaluation that sees what matters
Most eval frameworks optimize for accuracy on a benchmark. That tells you almost nothing about how a system fails in the real world. I design evaluations that surface the failure modes that matter—for the specific people, in the specific context, with the specific stakes involved.
Who's in the loop, and why
Automation decisions are human decisions. When I help a team build a pipeline or a product, I ask: where does a person need to be? Not because AI can't handle it—but because some decisions shouldn't be fully delegated, and some errors have consequences that automation can't absorb.
Data lineage and consent
Voice data, in particular, carries serious provenance questions. My background in ASR and training data pipelines means I take dataset lineage seriously—where did this data come from, who consented, and what are the downstream obligations? These aren't just legal questions; they're design constraints.
Building for breadth, not just scale
My work in conservation technology and community tools has shaped how I think about who AI is built for. "Works at scale" often means works for the median user in the training distribution. I help teams ask harder questions about who gets left out, and what it costs.
I write about this stuff
Production AI, responsible deployment, the gap between what demos show and what systems do in the wild. Occasionally: conservation technology, building for communities that don't usually get built for, and what the outdoors teaches you about systems thinking.
No SEO bait. No "10 prompts that will change your life." Just things I'm actually thinking about.
Read on SubstackGet in Touch
Ready to build something? Let's talk about your project.
Let's work together
Whether you need a one-day AI readiness assessment, an ongoing technical partner, or a workshop for your team—I'd love to hear what you're working on.
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linkedin.com/in/elliot-bearden -
Portland, OR • Remote & available globally