Hire an AI Software Engineer
I add AI to real products — OpenAI/LLM features, retrieval (RAG) and agentic workflows wired into your SaaS with cost control, evals and guardrails. Not a demo: AI features your users can trust and your finance team can predict, built by a senior software engineer who owns the whole app around them.
The hard part of shipping AI isn't the prompt — it's grounding output in your data, constraining it with tools and schemas, keeping the bill predictable, and knowing whether it's actually working. I've used AI integration to cut support volume by 30%, and I build the surrounding product (auth, data, UI) so the feature is a real capability, not a toy.
What you get
- OpenAI / LLM API integration into existing products
- Retrieval-augmented generation (RAG) over your own data
- Agentic workflows and AI-assisted automation
- Cost control: caching, streaming, right-sized models, token budgets
- Evals, guardrails and prompt-injection defense
How I work
We target one feature where AI clearly earns its cost — not AI for its own sake. That's what makes it shippable and measurable.
Retrieval over your data, tools/schemas to constrain output, and an eval set to measure quality before and after changes.
Caching, token budgets and monitoring so the feature is reliable and the monthly bill doesn't surprise anyone.
Integrated OpenAI APIs into a production SaaS and reduced support tickets by 30%. Designed RadiologyJobs' AI-recruiter matching layer (postings scanned and pushed to saved candidate searches), and I build with agentic tooling in my own delivery workflow, with explicit cost and quality controls.
Tech stack
Related work
FAQ
How do you keep AI costs from running away?
Right-size the model per task, cache aggressively (identical inputs shouldn't re-spend), stream to cut perceived latency, and set token budgets with monitoring. Cost is a design constraint from day one, not a surprise on the invoice.
What is RAG and do I need it?
Retrieval-augmented generation grounds the model in your own data instead of its training set — you need it whenever answers must reflect your documents, catalog or knowledge base. It's the difference between a plausible answer and a correct one.
How do you know the AI feature actually works?
Evals. I build a representative test set and measure quality against it, so a prompt or model change is a measured decision, not a vibe. Guardrails and prompt-injection defenses run alongside.