Hire Senior AI Engineers.
Engineers who ship LLM features, RAG pipelines, and AI agents into production — not demo videos.
80+
Products shipped
8+ yrs
In production
5–10 days
Time to start
24h
Reply time
Overview
AI engineering in 2026 is about reliability, cost, and evals — not which model is hot this week. Our AI engineers have shipped LLM features into production for SaaS platforms, customer support automation, internal tools, and content generation pipelines. They know how to design a RAG system that doesn't hallucinate, when to fine-tune vs prompt vs retrieve, how to build evaluation harnesses that catch regressions before users do, and how to keep token costs predictable under load. We use the AI SDK, MCP servers, and provider-agnostic patterns by default — your code shouldn't be coupled to one vendor.
Why Techyor.
Senior. Specialist. Shipping.
Provider-agnostic by default
Vercel AI Gateway or LangChain abstractions — your code shouldn't couple to one model provider. Switch from Claude to GPT-5 to Gemini in a config change, not a rewrite.
Evals as a first-class artifact
Every AI feature ships with an evaluation harness — golden datasets, automated scoring, regression detection. We can prove the feature got better, not just feels better.
Cost-aware, latency-aware
We design for unit economics from day one. Caching, model routing (cheap model first, escalate to flagship), streaming for UX wins. Your bill is predictable.
What they cover.
Skills and stack.
Capabilities
- LLM integration — OpenAI, Anthropic Claude, Gemini, open-weight (Llama, Mistral)
- RAG pipelines — chunking, embedding, retrieval, reranking, evals
- Agent frameworks — Vercel AI SDK, LangGraph, CrewAI, custom orchestration
- Vector databases — pgvector, Pinecone, Weaviate, Qdrant, Chroma
- Fine-tuning — LoRA, QLoRA on open-weight models
- Prompt engineering — few-shot, chain-of-thought, structured output
- Evaluation — Promptfoo, LangSmith, custom eval harnesses
- Observability — LLM tracing, token usage tracking, latency monitoring
- MCP — building servers, integrating clients into AI workflows
Tech stack
The people behind the work.
Hire them directly.
Selected work.
AI Engineers in the wild.
A small slice of 5 projects where this role led the work.
Monster Fairings
A global e-commerce platform with AI-powered visualization, managing 5000+ fairings and serving riders across seven countries.
Firmable
AI-driven B2B Sales Intelligence platform providing buying signals and sales insights
SimpleTalk AI
Enterprise AI voice sales platform handling 147K+ calls across 12 languages, with multi-channel agents, white-label tenancy, and deep CRM integrations.
ConvertBankStatement
A specialized PDF-to-Excel conversion platform powered by a custom Python OCR engine, supporting 250+ bank formats and 30+ currencies for accountants and finance teams worldwide.
AiMusicService
AI-integrated music services e-commerce platform
How you can hire.
Three ways to engage.
Dedicated Full-Time
A senior developer working exclusively on your product, embedded into your team, sprint cycle, and rituals. Best for roadmap-driven work with continuous shipping.
Part-Time / Fractional
Two to three days a week of senior expertise. Ideal for early-stage teams, technical advisory, code review, or supplementing an existing in-house team.
Fixed-Scope Project
Defined milestones, outcomes, and timeline. Best when the brief is clear and you want a predictable budget. Includes design, QA, and handover.
How it works.
Simple. No theatre.
- 01
Quick intro call
A 30-minute conversation. Tell us what you're building, the stack you're on, and where you'd like help. No deck, no obligation.
- 02
We share our experience
We walk you through relevant projects we have shipped, the approach we would take, and the trade-offs to expect. Honest about what we are great at — and what we are not.
- 03
Start work
Mutual NDA, a clear scope, and a start date. We begin shipping that week. Daily updates and a single point of contact throughout.
What clients say.
On ai engineer work.
Verified Upwork reviews from teams we've shipped for.
"Great freelancer — proactive, thinks outside the box, and responds quickly. I hired them for the second time and will definitely hire again for future projects. Thanks!!"
FAQ.
Common questions.
RAG vs fine-tuning — when do you choose what?
RAG by default: it's cheaper, faster to iterate, and works for 80% of 'AI knows our docs' use cases. Fine-tune when you need behavior change (tone, format) or domain knowledge that's awkward to retrieve. We've often combined the two — fine-tune for behavior, RAG for facts.
Which model do you recommend?
It depends on the task. Anthropic Claude Opus 4.7 for the hardest reasoning tasks. Sonnet 4.6 for most production work. Haiku 4.5 for high-volume, low-latency. GPT-5 family for tool use and structured output. We design code to be model-agnostic so you can switch.
How do you measure AI feature quality?
Evaluation harnesses with golden datasets. Automated scoring (LLM-as-judge for subjective, rule-based for structured). User feedback signals. We don't ship AI features without a way to measure regressions.
Are you safe with hallucinations?
We design for them. Citation requirements (every claim links to a source), structured output validation, refusal patterns when confidence is low. Hallucinations don't disappear — we constrain where they can do damage.
Can you build AI agents?
Yes — production agents, not demos. Tool use, plan-execute loops, human-in-the-loop checkpoints, durable execution (Vercel Workflow, Inngest). We've shipped agents that take real actions on real systems.
What about cost control?
Token budgeting per request, prompt caching (Anthropic), model routing (Haiku first, escalate), response caching for deterministic queries, streaming so users see progress. AI cost is engineering, not magic.
MCP — what is it and do you use it?
Model Context Protocol — Anthropic's open standard for AI tool integration. Yes, we build MCP servers for clients (data sources, internal APIs) and integrate MCP into agent workflows. It's becoming the default plumbing for AI integrations.
How fast can a developer start?
For most roles, within 5–10 business days from the intro call. We pre-qualify our bench so onboarding is contracts and access — not a hiring search.
Do you sign NDAs and IP assignment agreements?
Yes — every engagement starts with a mutual NDA and a clean IP assignment clause. All work product is your property the moment it's written.
What time zones do your developers cover?
Our team primarily operates in IST with 4–6 hours of overlap with US Pacific, full overlap with EU, and full overlap with AU East. We commit to overlap windows in writing.
Related roles.
Also worth a look.
Ready to start?
Let's chat.
Tell us what you're building. We will reply within 24 hours, hop on a quick intro call, walk you through relevant work we have shipped, and take it from there.
Prefer email? info@techyor.com
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