Hourly
Rate on request
- Vetted senior AI engineer
- 4h+ daily timezone overlap
- Weekly progress reports
- Two-week risk-free start
[ AI developer staffing services ]
Every AI specialization, staffed from one vetted network
Staff augmentation for LLM, machine learning, computer vision, NLP, MLOps and AI product integration work — not a recruiting placement, not an outsourced project. Dedicated engineers who join your team and your repos, screened through the same four-stage process that fewer than 4% pass.
- AI specializations
- 6AI specializations
- acceptance rate
- 4%acceptance rate
- to first match
- 48hto first match
- AI developers and engineers
- 0+
- AI projects delivered
- 0+
- Client retention rate
- 0%
- Average response time
- 0h
The model
Staff augmentation, not recruiting or outsourcing
A dedicated AI engineer becomes part of your team on your terms — different from a recruiting agency that hands you a resume, and different from an agency that delivers a project you never touch.
You manage the work
Your engineer joins your sprints, your standups and your tools. You set priorities day to day — this isn't outsourced delivery you hand off and wait for.
We manage the talent pipeline
Sourcing, screening and the four-stage vetting process happen before you ever see a profile. You're not running a recruiting funnel — we already ran it.
You own everything
Code lives in your repositories, IP is assigned in writing, and there's no placement fee or percentage-of-salary cost like direct-hire recruiting.
Capabilities
Six specializations, one point of contact
Jump to any specialization for what's included, typical stack and the best-fit engagement model.
LLM & GenAI engineering
A dedicated engineer who ships production LLM features, not a demo — evaluation and cost guardrails built in from day one.
Machine learning engineering
An engineer who owns the full model lifecycle — architecture, offline evaluation, rollout and retraining — measured against your metrics.
Computer vision
An engineer who takes vision systems from labeled data to real-time inference, on cloud or edge.
Natural language processing
An engineer who builds document and search systems tuned to your domain vocabulary, not a generic off-the-shelf model.
MLOps & model deployment
An engineer who keeps models running after launch: CI/CD, monitoring, drift detection and cost control.
AI product integration
An engineer who ships the AI feature end to end — API, data plumbing, guardrails and UX — inside your existing product.
Specialization
LLM & GenAI engineering
Chat assistants, copilots, agents and RAG pipelines built on GPT, Claude, Gemini and open-source models — with evaluation baked in from day one.
- RAG pipeline design and retrieval tuning
- Fine-tuning and evaluation harnesses
- Guardrails, prompt and cost management
- Production monitoring for hallucination and drift
Typical stack
- OpenAI
- Claude
- Gemini
- LangChain
- RAG pipelines
- vLLM
LLM · Fintech
A Series B payments company's RAG assistant now auto-resolves 72% of support tickets, live in 6 weeks.
Seniority
Senior LLM engineers, 5+ yrs shipping production language-model systems
Best-fit engagement
Monthly or fixed price, depending on scope
72%
tickets auto-resolved
6 weeks
time to production
5+ yrs
avg engineer experience
Fintech
deep domain expertise
ML · Marketplace
A recommendation-engine rebuild lifted click-through 18% within one quarter.
Specialization
Machine learning engineering
Custom models for prediction, ranking and recommendation — trained, tuned and validated against your business metrics, not just benchmarks.
- Model architecture and feature engineering
- Offline evaluation against your business metrics
- A/B test design for model rollouts
- Ongoing retraining and performance monitoring
Typical stack
PyTorch, TensorFlow, JAX, Hugging Face, scikit-learn, XGBoost
Seniority
Mid-to-senior ML engineers, 4+ yrs in applied modeling
Best-fit engagement
Hourly to start, scale to monthly as models move to production
Specialization
Computer vision
Detection, segmentation, OCR and visual inspection systems that run in the cloud or on the edge — from prototype to real-time production.
- 01Dataset curation and annotation pipelines
- 02Model training for detection, segmentation and OCR
- 03Edge deployment and latency optimization
- 04Real-time monitoring for drift and false positives
Typical stack
PyTorch · TensorRT · Edge AI · C++
Seniority
Senior CV engineers with production edge-deployment experience
Best-fit engagement
Monthly, typically paired with an MLOps specialist
Computer vision · Manufacturing
Edge defect-detection models now run at 99.2% recall across 12 production lines.
Specialization
Natural language processing
Classification, extraction, summarization and search over your documents — multilingual, domain-tuned and measurable.
- Document classification and information extraction
- Semantic search and embeddings over your content
- Summarization tuned to your domain vocabulary
- Multilingual support where needed
Typical stack
- Hugging Face
- Pinecone
- Weaviate
- Embeddings
NLP · Legal tech
Contract clause extraction cut manual review time by 65% for a legal-tech client.
Seniority
NLP engineers with domain-tuning and evaluation experience
Best-fit engagement
Hourly or fixed price for well-scoped document pipelines
65%
review time cut
8 weeks
time to production
4+ yrs
avg engineer experience
Legal tech
deep domain expertise
MLOps · SaaS
Re-platformed model serving cut inference costs 60% at 30M predictions/day.
Specialization
MLOps & model deployment
CI/CD for models, monitoring, drift detection and cost-efficient serving on AWS, GCP or Azure — so models keep working after launch.
- Model CI/CD and versioning
- Serving infrastructure and autoscaling
- Cost optimization for inference at scale
- Drift detection and automated retraining triggers
Typical stack
AWS SageMaker, GCP Vertex, Kubernetes, MLflow, Docker, Terraform
Seniority
MLOps architects, 6+ yrs running production ML infrastructure
Best-fit engagement
Monthly for ongoing infrastructure ownership
Specialization
AI product integration
Embed AI into your existing product: APIs, data plumbing, guardrails and UX — shipped by engineers who also speak backend and frontend.
- 01API design for AI features inside your product
- 02Data plumbing between models and your existing systems
- 03Guardrails, rate limiting and fallback handling
- 04UX for AI features — loading states, confidence, feedback loops
Typical stack
APIs · Postgres · Kafka · Airflow
Seniority
Full-stack engineers fluent in both AI systems and product engineering
Best-fit engagement
Fixed price for scoped integrations, monthly for ongoing AI feature work
Integration · SaaS
An AI copilot embedded into an existing dashboard shipped in 5 weeks, no separate app needed.
Coverage
Every engagement model, stack, role, industry and region we staff for
Beyond the six headline specializations: the engagement terms, frameworks, job titles, verticals and regions our vetted network already covers.
Engagement model
- Hourly
- Monthly
- Fixed-cost
Cloud platforms
- AWS
- Google Cloud
- Microsoft Azure
Frameworks
- PyTorch
- TensorFlow
- Keras
- scikit-learn
Languages
- Python
- TypeScript
- Go
- SQL
Generative AI / LLM stack
- OpenAI API
- LangChain
- LlamaIndex
- RAG
- AI Agents
- Vector Databases
NLP stack
- Hugging Face Transformers
- spaCy
- NLTK
- BERT
Computer vision stack
- OpenCV
- YOLO
- Vision Transformers
- Image Segmentation
Data engineering stack
- Apache Spark
- Apache Kafka
- Apache Airflow
- Snowflake
- Databricks
Role
- AI App Developer
- AI SaaS Developer
- AI Integration Developer
- AI Automation Developer
- AI Chatbot Developer
- Machine Learning Engineer
- AI Solutions Architect
- AI Consultant
Industries
- Healthcare
- Fintech
- E-commerce
- Real Estate
- Logistics
- Edtech
- Startups
Locations
- USA
- UK
- Canada
- India
- Australia
- Germany
- UAE
- Singapore
Remote-first, overlapping US, European and APAC business hours. Top countries also get combo hire-[tech-stack]-developer location pages.
Vetting
The four stages every AI developer goes through before joining our team
Every developer on our team goes through the same process before they join us. Fewer than 4 in 100 candidates make it through.
01
Portfolio & production-history review
We look at systems a developer has actually shipped, not just a resume or a list of frameworks.
02
Live ML system-design interview
A senior AI engineer talks through architecture decisions and tradeoffs on a real-world scenario, live.
03
Hands-on build assignment
A scoped build task, scored by senior reviewers against production-quality criteria, not puzzles.
04
Communication assessment
Can they explain a tradeoff clearly and work async with a team they have never met? We check this too.
Engagement models
Simple pricing, no recruiting fees
Choose how you work with us. Every model includes vetting, matching and replacement guarantees.
Most common
Monthly
Rate on request
- Everything in hourly
- Embedded in your team rituals
- Priority replacement guarantee
- Free technical lead oversight
Fixed price
Quote on request
- Fixed scope, fixed price
- Full pod: ML engineer, backend engineer and MLOps specialist
- Milestone-based payments
- Post-launch support included
Communication
Visibility into the work, without micromanaging it
You always know what's happening and who to ask — even though the engineer works inside your team, not ours.
Daily written updates
Async updates land in your Slack every day, so the rest of your day stays uninterrupted and visible.
Weekly progress reports
A structured written report each week — what shipped, what's blocked, what's next — no status-meeting theater.
One point of contact
One agreement and one technical lead across every engineer on your account, with oversight included at the full-time tier.
Trust & compliance
The legal groundwork is already done
NDA signed before any technical discussion
Full IP assignment in every contract, in writing
Engineers work inside your repos, VPN and access controls
Comparison
Hire AI Developers vs. the alternatives
| Hire AI Developers | In-house hiring | Freelance marketplaces | |
|---|---|---|---|
| Time to start | Under 1 week | 5+ months | 1–4 weeks |
| Technical vetting | 4-stage, by senior AI engineers | Your team's time | Self-reported profiles |
| Cost of a bad hire | Zero, free replacement | Significant cost, months lost | Your risk |
| Recruiting fees | None | 20–30% of salary | 15–20% platform markup |
| IP & NDA handling | Standard in every contract | Standard | Varies per freelancer |
| Scale team up/down | 2 weeks' notice | New hiring cycle | Re-search each time |
Case files
Specializations at work, measured in outcomes
ML engineering · Retail
Demand forecasting that cut stockouts 31%
-31%stockout rate
8 wksto production
NLP · Insurance
Claims document extraction at 94% field accuracy
94%field accuracy
-70%manual entry time
Integration · HealthTech
Patient intake copilot embedded in 5 weeks
5 wksto launch
0new apps to maintain
MLOps · SaaS
Model serving costs cut 60% at 30M predictions/day
-60%serving cost
30Mdaily predictions
FAQ
Questions about choosing a specialization
Can I hire for just one specialization, like computer vision? — Yes. Every engagement is scoped to a single specialization by default — LLM/GenAI, ML engineering, computer vision, NLP, MLOps, or product integration. Most clients start with one engineer and add specializations as the project grows.
How is pricing different for a project versus a dedicated hire? — Dedicated hires (hourly or full-time) are billed per engineer, as shown in our engagement models. Project-based work is scoped and quoted after a free technical consultation, with milestone-based payments instead of a monthly rate.
Can I put multiple specializations on one team? — Yes — we assemble pods that mix specializations (for example an LLM engineer, an MLOps specialist and a backend engineer) under a single contract, with one point of contact and unified reporting.
Do I need to know exactly which specialization I need before reaching out? — No. A 30-minute call with a technical lead is enough to map your stack and skills gap to the right specialization — you don't need to arrive with a job description.
Can a developer move between specializations mid-engagement? — If your project's needs shift, we'll match you with an engineer for the new specialization rather than stretching your current engineer outside their vetted strength. Swaps follow the standard two weeks' notice.
[ Start here ]
Tell us what you're building. Meet your developers this week.
One short form. A technical lead reads it, assigns the right AI developer from our team, and replies within one business day, with a developer ready to start, not a sales deck.
- Free 30-minute technical consultation
- 2-3 available AI developers within 48 hours
- Two-week risk-free trial with every developer
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