AI Engineer for Hire — LLMs, Fine-Tuning, Production AI Systems
An AI engineer designs, builds, and operates AI systems in production — eval harnesses, observability, MLOps, drift detection, cost guardrails, the works. I work as a senior AI engineer with 25 years in computer science, U.S. Army veteran, single-principal delivery. I ship LLM pipelines, fine-tuned models, RAG systems, and the production tooling that keeps them honest after launch.
AI engineer vs AI developer vs ML engineer
The titles overlap, the discipline differs at the edges. AI developer builds the AI system end-to-end (model + pipeline + UI). AI engineer emphasizes the production-operations side — the eval harness, the monitoring, the deploy pipeline, the cost guardrails. ML engineer is the older title and usually implies a training-heavy practice (feature engineering, training infra, model architecture). I do all three; the title on this page emphasizes the production-operations posture because that is what most "we shipped an AI demo but it does not work in production" engagements need.
What I build as an AI engineer
- Eval harnesses. Golden sets, regression suites, A/B and shadow deployment, observable metrics that map to buyer KPIs (not vanity scores).
- Production LLM pipelines. Streaming and batch inference, request budgets, retry / fallback logic, cost telemetry.
- Fine-tuning workflows. LoRA, QLoRA, and full fine-tunes with reproducible runs, versioned datasets, signed model artifacts.
- RAG retrieval. Vector stores, citation-first answers, retrieval-quality eval, source-document versioning.
- Observability + monitoring. Drift detection, prompt-quality drift, response-distribution drift, latency, cost-per-request, error-rate dashboards.
Production AI — what "shipped" actually means
An AI demo is a notebook. An AI production system is a service with an SLO, a budget, a rollback plan, and an on-call rotation. Most buyers I see have the demo and need the production system. Building from demo to production is the AI engineer's job: write the eval harness so quality has a measurable baseline; write the monitoring so quality drift is detected before customers report it; write the deploy pipeline so a new model can be promoted (and rolled back) without an incident.
Who hires me as an AI engineer
VPs of engineering whose ML team has a working notebook but no production deploy. CTOs whose AI roadmap depends on a third-party API and who need a self-hosted alternative because of compliance or cost. Founders whose AI feature works in the demo but breaks under real-world traffic patterns. Microsoft-shop enterprises whose Azure ML stack works for training but does not have the production-deploy plumbing for an LLM workload.
What is the difference between an AI engineer and an AI consultant?
The AI consultant produces a roadmap; the AI engineer produces a running system. Many engagements need both — I do both — but the deliverables are different. If you need a strategy document and a build/buy/wrap matrix, the AI consulting page is the right entry. If you need an eval harness, a deploy pipeline, and a model in production, this page is.
Are you available freelance for AI engineering?
Yes — one engagement at a time, fully present. AI engineering engagements typically run 2-6 months because the eval / deploy / monitoring loop is real work.
What is your hourly rate?
Bespoke, scoped per engagement, written into SOW before contract. AI engineering is typically priced per-phase (eval harness; deploy pipeline; monitoring; cost optimization) rather than hourly.
Do you build the model, or only the production tooling around it?
Both. The eval harness comes first (sprint zero), then the model (sprint one), then the deploy pipeline (sprint two), then the monitoring (sprint three). The shape depends on what the buyer already has — if there is a working model and a missing deploy story, sprint zero is the deploy plan.
Can the system run on our own hardware?
Yes — self-hosted deployment is one of the more common engagement shapes. Healthcare, energy, and financial-services buyers whose compliance posture rules out third-party APIs lean on this regularly.
Scope This Engagement
Single principal, plan first, working code on every checkpoint.
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