Fractional · Embedded · Hands-on
Hire a lead
AI engineer.
I'm Ergini, a senior AI engineer who can own your AI technical direction: architecture, model and provider choices, RAG and agent design, evaluation, and the cost and latency budgets that keep it shippable. Hands-on, not manager-only. I set the standard by writing the hard parts and reviewing the rest.
A lead who still writes the code
Most teams that need a lead AI engineer do not need another manager. They need one senior person who has actually shipped AI to production to make the calls everyone else is guessing at: which model, retrieval or fine-tuning, agent or workflow, how to evaluate it, and how to keep cost and latency sane. Those decisions are cheap to get wrong early and expensive to unwind later.
I take that role as a hands-on IC. I am in the repo making the architecture real, writing the eval harness, and holding the bar in review, while leveling up your engineers so the capability stays after I leave. I have shipped six AI products end to end and contributed to two more, so the direction I set comes from production scars, not slide decks.
What I own
AI system architecture
The end-to-end design: where the model sits, how retrieval and tools connect, where humans approve, and how it all scales without turning into spaghetti.
Model and provider selection
OpenAI, Anthropic Claude, or open-source, chosen on capability, cost, latency, and lock-in, not on hype. With a migration path when the landscape shifts.
RAG and agent design
Deciding when you need retrieval, when you need an agent, and when a plain deterministic workflow is the right answer. Then designing the one you actually need.
Evaluation strategy
The eval harness, datasets, and metrics that tell you whether a change made things better or worse, so you stop shipping on vibes.
Cost and latency optimization
Caching, routing, model tiering, and prompt discipline to get response time and per-query cost into a range the business can live with.
Code review and mentoring
Holding the quality bar on every AI pull request and leveling up your engineers so the capability stays in the team after the engagement ends.
Ways to bring me in
Fractional lead
A few days a week owning AI direction across an existing team - setting architecture, reviewing AI pull requests, unblocking the hard calls, and keeping the eval and cost story honest.
Embedded for a build
Joining your team for the duration of a specific AI build, in your repo and your process, leading the AI layer hands-on from design through launch.
Architecture and audit
A focused engagement to review your existing AI system - eval gaps, cost and latency, retrieval quality, guardrails - and a written plan your team can execute. See the AI SaaS architecture write-up.
Frequently asked questions
What does lead mean here, exactly?
It means owning the technical direction of your AI work: the architecture, the model and provider choices, the evaluation strategy, and the standards the rest of the team builds to. I set the direction and I hold the bar in code review. It does not mean people management or running your sprint board.
Do you write code, or just advise?
Both. I am a hands-on senior IC first. On most engagements I am in the codebase: designing the RAG or agent layer, writing the eval harness, fixing the cost and latency problems, and reviewing pull requests. The leadership comes from doing the hard parts and setting the patterns, not from sitting above the work.
Fractional, embedded, or a full engagement?
Whatever fits. Fractional is a few days a week to own AI direction across an existing team. Embedded is joining your team for the duration of a build. A full engagement is me owning an AI system end to end. We pick the shape on the discovery call based on what your team is missing.
How do you work alongside an existing engineering team?
I plug into your stack, your repo, and your review process. I take the AI-specific decisions off your engineers' plates - retrieval design, eval, guardrails, provider tradeoffs - while they keep shipping product, and I level them up through review and pairing so the knowledge stays after I leave.
What kinds of decisions do you own?
Model and provider selection across OpenAI, Anthropic, and open-source. RAG versus fine-tuning. Agent versus deterministic workflow. The evaluation and observability strategy. Cost and latency budgets. Prompt and guardrail standards. And the architecture that ties it all together so it survives contact with production.
What does a lead engagement cost?
AI agent and leadership engagements typically run $12K-$60K, and ongoing fractional or advisory time is $100/hr. I set the exact range around your requirements after a free 30-minute discovery call.
Get a sense of how I think: workflow vs agent, choosing an eval framework, or fine-tuning vs RAG.