Senior AI engineering · production delivery
Hire an AI engineer for production-grade agent systems, backend integrations, and AI-powered web apps — with architecture ownership and delivery rigor.
An AI developer gets a feature working. An AI engineer makes it survive real users, weird inputs, model drift, and a 2am page. If your AI feature has to clear uptime, data-access, or cost expectations beyond a prototype, the hard part is not the prompt — it is the system around it: typed contracts, eval harnesses, observability, retries, and release discipline. That layer is what I build.
Architect the system around the model: tool boundaries, data-access scopes, typed contracts, idempotent operations, and deterministic fallbacks. The model is one component, not the whole product.
Build agent workflows and model-backed APIs that connect to real infrastructure — auth, databases, queues, third-party services — through MCP servers and typed endpoints instead of brittle glue.
Golden-task suites, regression runs on every prompt/model/harness change, and release gates so you can iterate without silently shipping worse output.
Structured logging, traces, token and latency budgets, drift alerts, and model routing — so you can debug what the agent actually did and keep cost-per-task predictable.
Failure-mode handling, rate limits, spend caps, security review, and runbooks before the feature reaches users. The gap between a demo and a product is mostly this work.
Hire an AI developer when the priority is shipping features fast and the cost of a wrong output is low. Hire an AI engineer when the AI sits on real infrastructure, must meet reliability or compliance expectations, and a bad output has a real cost. Many teams start with a developer and bring in engineering ownership once the feature becomes load-bearing.
Idempotent tool design, structured errors instead of opaque exceptions, retries with backoff, an eval harness that catches regressions before they ship, and observability that lets you reconstruct any run. Skip any one of these and a working demo becomes an outage.
As soon as you intend to change the prompt, swap the model, or let more than a handful of users touch the feature. Without evals you are guessing whether each change made quality better or worse. With them you know within minutes, which is what lets the system keep improving safely.
For focused products, yes — and it is usually better. One accountable engineer owning the model layer, backend, data access, and deployment removes the handoff gaps where AI reliability problems hide. For larger programs, that engineer sets the standards the rest of the team builds against.
Define the workflow, the data the model can touch, the tools it can call, the failure modes, and the reliability bar before any model code is written.
Typed tool schemas, auth scopes, structured errors, and a golden-task eval suite so quality is measurable from the first commit.
AI-assisted implementation for velocity, with senior review on architecture, security, data access, and cost on the way in.
Retries, rate limits, spend caps, traces, and drift alerts, then a clean handover with runbooks your team can operate.
50+
Real systems with auth, data, agents, billing, and ongoing maintenance — not sandbox demos.
Eval-first
Regression suites mean you can change a prompt or swap a model and know within minutes whether quality moved.
12 yrs
Production accountability under compliance, stakeholder pressure, and mission-critical uptime.
Bring the workflow, the stack, and the reliability bar it has to clear. I'll map the system, the eval strategy, and the production risks before any code is written.
Move from a prototype that works in the demo to a feature that survives real users, with one engineer owning the whole system.
Book a build callBring in production AI engineering for the parts your team hasn't shipped before: evals, observability, agent reliability, and cost control.
See agent engineeringGet the eval, observability, and review gates your team should adopt — proven on shipped systems, not slideware.
Review the operating modelAn AI developer gets a feature working; an AI engineer makes it survive real users, model drift, partial tool failures, and production load.
The difference between a demo and a product is five things: idempotent tools, structured errors, retries, an eval harness, and observability.
Hire an AI engineer when the model sits on real infrastructure and a bad output has a real cost — reliability, data access, and uptime become the actual job.
An eval harness is worth building the moment you intend to change the prompt, swap the model, or let more than a handful of users touch the feature.
Production AI cost stays predictable through model routing, prompt caching, and batching — engineered deliberately, not discovered on the invoice.
The fastest way to ship reliable AI is one accountable engineer owning the model layer, backend, data access, and deployment end to end.
Engineering, Not Prompt Theater
Model output is only one layer. I own the system around it: data boundaries, retries, rollout safety, and maintenance path.
Shipped AI Products
Production agents, MCP servers, and full-stack systems in active use — not sandbox demos.
Fast Without Fragile
AI tooling accelerates implementation, while architecture and review keep the product reliable.
Share your current stack, delivery deadline, and the AI workflow you need. I'll reply with concrete scope and a build path.
Get in touchAn AI developer often focuses on feature delivery speed. An AI engineer is accountable for the full system: architecture, reliability, safety boundaries, observability, and long-term maintainability. Most production projects need both skill sets, but if the workflow is complex or high-stakes, engineer-level ownership matters.
An AI engineer builds products on top of foundation models like Claude and GPT — tool use, agents, RAG, MCP integrations, and the reliability layer around them. An ML engineer trains and serves custom models — data pipelines, feature stores, training, and inference infrastructure. Most teams shipping on top of LLM APIs need an AI engineer; you need an ML engineer when the model itself is your differentiator and you are training it.
Project engagements typically range from $8,000–$30,000 depending on complexity, integrations, and reliability requirements. Embedded monthly engagements generally run $5,000–$12,000 for part-time senior ownership.
Both — and the evals and observability are the point of hiring an engineer rather than a developer. Every agent ships with a golden-task suite plus structured logging and traces, so you can change a prompt or model and immediately see whether quality moved, and debug exactly what any run did in production.
Yes — rescue and hardening is a common first engagement. I audit the failure modes, add an eval harness, fix tool boundaries and error handling, add retries and spend caps, and instrument the system so it is debuggable. Most hardening passes take one to three weeks depending on how the feature was built.
Hire an AI engineer when your product needs real integrations, strict data controls, quality measurement, or uptime expectations that exceed prototype standards.