Stop sifting through fake CVs. Get 3 rigorously vetted AI/ML engineers - RAG, LLMs, MLOps, PyTorch - shortlisted in 72 hours with full managed delivery.
Trusted by AI-forward teams
The AI talent market is thin, expensive, and full of fraudsters. Our vetting and managed delivery model solves every one of those problems.
Upwork is flooded with fake AI profiles. Every OTF engineer passes a live ML coding screen, a system design review, and a real project evaluation - not a quiz.
Enterprise AI talent is scarce. Our AI scans 340+ vetted engineers and a senior ML lead validates each match manually - so you get quality fast, not fast trash.
Toptal charges 40-60% hidden markups. We charge a transparent flat 15% management fee on the developer's rate. $20-$200/hr. No surprises.
Enterprise buyers need accountability for model outputs. We provide weekly delivery reports, milestone tracking, and dispute resolution baked into every engagement.
No long-term contracts, no exit penalties. Engagements run on 14-day notice. You own 100% of all IP, models, and data pipelines under a tri-party contract.
Unlike Upwork or Fiverr, we specialise exclusively in software and IT. Every developer in our pool is a career engineer - not a side-hustler or generalist.
Anonymised profiles - real skills, real output. Every engineer has passed a live coding screen and system design interview. Your shortlist will be tailored to your brief.
Priya S.
Senior LLM / RAG Engineer
Marco D.
MLOps / Platform Engineer
Yuki T.
Generative AI / Fine-tuning Specialist
Kwame A.
Data Scientist / ML Engineer
Sofia R.
Computer Vision Engineer
Arjun V.
NLP / Agentic AI Engineer
These are sample profiles. Your shortlist is built to match your exact brief and stack.
$0 matching fee · 72 hrs · 14-day guarantee
No recruiters. No CV screening. No bidding wars. Just a fast, structured path from "I need an ML engineer" to "model is in production."
Free to post · $0 matching fee · Shortlisted in 72 hrs
From LLM integrations to production MLOps - our engineers ship real AI systems, not demos. Here is what they deliver.
Don't see your exact use case? Post your brief and we will match you with the right specialist.
Our engineers build LLM systems that actually work in production: low hallucination, controlled outputs, and measurable ROI.
Retrieval-Augmented Generation with vector DBs
PEFT, LoRA, QLoRA on custom datasets
Multi-agent workflows (AutoGen, CrewAI, LangGraph)
Pinecone, Weaviate, Chroma, pgvector
Systematic evaluation and prompt optimisation
OpenAI, Anthropic, Cohere, Mistral, Ollama
Our MLOps engineers handle the full lifecycle - experiment tracking, feature engineering, model serving, monitoring, and automated retraining.
Regulated industries and enterprise buyers require more than a working model. Our engineers understand safety, auditability, and regulatory compliance from day one.
Data minimisation, pseudonymisation, and right-to-erasure compliance for ML training pipelines.
Risk classification, transparency requirements, and conformity assessments for high-risk AI systems.
Red-teaming, output filtering, hallucination reduction, and responsible AI guardrails for LLM deployments.
SHAP, LIME, and Grad-CAM integration for regulated industries - finance, healthcare, insurance.
AI/ML roles vary dramatically by seniority. Use this matrix to identify the level you need for your project, then let us match you accordingly.
1-3 yrs experience
$20-$55/hr
per hour
3-6 yrs experience
$55-$110/hr
per hour
6+ yrs experience
$110-$200/hr
per hour
Not sure which level you need? Post your brief and our AI matching engine will recommend the right seniority for your scope and budget.
Hiring the wrong role costs you 3-6 months. Here is the definitive breakdown - so you post the right brief and get the right engineer.
Building AI-powered products using existing foundation models
Hire This Role When:
You have a product that needs AI/LLM features built on top of existing models.
Training, deploying, and maintaining machine learning models
Hire This Role When:
You need to train or fine-tune a model, or productionise an existing one.
Extracting insights from data and building analytical ML models
Hire This Role When:
You need data-driven decision making, analytics models, or business intelligence.
Still unsure? Describe your project and our team will recommend the right role - free, in 24 hours.
AI is the most CV-faked category on freelance platforms. Here is what to ask, what to watch for, and how to benchmark cost - whether you hire through us or anywhere else.
Explain the difference between RAG and fine-tuning. When would you use each?
Why this works: Tests architectural decision-making - a fundamental senior AI engineering judgement call.
How would you reduce hallucinations in a production LLM application?
Why this works: Reveals real-world safety experience vs theoretical knowledge. Fake CVs fail here.
Walk me through how you would set up an MLOps pipeline for a model that retrains weekly.
Why this works: Tests production thinking - tooling choices, CI/CD, monitoring, and rollback strategies.
How do you evaluate a retrieval-augmented generation system? What metrics do you use?
Why this works: Separates engineers who have shipped RAG from those who have only read about it.
Describe a model that underperformed in production. What went wrong and how did you fix it?
Why this works: Past production failures are the best signal for future production readiness.
How would you fine-tune a 7B parameter model if you only have limited GPU budget?
Why this works: Tests practical knowledge of PEFT, LoRA, QLoRA - common real-world constraint handling.
💡 OTF tip: Skip the interview prep - every OTF engineer has already passed a live coding screen equivalent to the above. Your shortlist arrives pre-validated.
Cannot explain their model's performance metrics
Any real ML engineer knows accuracy, precision, recall, F1, AUC - and when to use each. Vague answers = tutorial-level knowledge.
Has never deployed a model to production
Notebook models are not production models. Ask specifically: "What serving infrastructure did you use?" Silence is a red flag.
Claims deep expertise in everything AI
LLMs, CV, RL, MLOps, and data engineering are different specialisms. A candidate claiming all of them without a strong primary area is almost certainly inflating their CV.
Cannot articulate trade-offs
Every design decision has trade-offs (speed vs accuracy, cost vs quality). Engineers who cannot articulate them are following tutorials, not thinking architecturally.
No GitHub, papers, or demonstrable project output
AI is an evidence-based field. If a candidate cannot point to code, a model card, or a published result, scrutinise their claims carefully.
AI salaries are inflated market-wide. Freelance rates reflect the same supply constraint - but give you flexibility. Use the full rates table below to plan your budget by region and seniority.
Junior
$20-$55/hr
Good for data prep, EDA, basic model implementation
Mid-Level
$55-$110/hr
Production models, RAG, MLOps pipelines
Senior / Lead
$110-$200/hr
Architecture, fine-tuning, enterprise compliance
All rates are developer take-home rates. Open IT Freelancers adds a flat 15% management fee - no hidden markups, no opaque pricing.
Eastern Europe
Poland, Romania, Ukraine
Strong ML talent pool; excellent English; CET/EET timezone
South Asia
India, Sri Lanka
Largest ML developer talent base globally; IIT / BITS graduates
South-East Asia
Singapore, Philippines, Vietnam
Strong Python and data engineering base; growing LLM expertise
Latin America
Brazil, Colombia, Argentina
US timezone overlap; good English; growing AI ecosystem
Middle East & Africa
South Africa, Egypt, UAE
Emerging talent base; strong for MLOps and data engineering
Western Europe
UK, Germany, Netherlands
Highest rates; ideal for regulated industries requiring EU presence
OTF Management Fee
+ 15%
Flat - on developer rate only
Matching Fee
$0
No upfront cost to get your shortlist
Replacement Guarantee
14 Days
Free swap if not the right fit
Rates are indicative ranges based on 2026 market data. Final rate is agreed directly with the developer. OTF adds a flat 15% management fee and nothing else.
For hiring AI/ML talent, vetting depth and delivery accountability are the two factors that matter most. Here is how the options stack up.
| Feature | Open IT Freelancers | Upwork | Toptal | In-House |
|---|---|---|---|---|
| Vetting depth | Live coding + system design + CV verification | Self-reported skills and portfolio | Live coding + domain interview | Full internal interview process |
| AI CV fraud protection | ✓ Specific AI/ML live screen required | ✗ Open marketplace - anyone can list AI skills | ✓ Vetting includes AI skills | Depends on interviewer's technical depth |
| Time to first shortlist | 72 business hours | Hours (but unvetted) | 5-14 days | 4-12 weeks |
| Pricing model | Developer rate + flat 15% fee | 5-10% client fee + 5-20% freelancer fee | Opaque markup (typically 40-60% above dev rate) | Salary + benefits + overhead (~1.5× base salary) |
| Upfront deposit | $0 | $0 (escrow when work starts) | $500 trial deposit | Recruiter fee: 15-25% of salary |
| Managed delivery | ✓ Weekly reports, milestones, dispute resolution | ✗ Buyer manages independently | ✗ Placement only - no managed layer | ✓ Full internal management |
| IP protection | ✓ Tri-party contract, immediate IP transfer | Varies by contract terms | ✓ Standard IP agreement | ✓ Employment contract |
| Model output accountability | ✓ Milestone-based delivery for AI outputs | ✗ No platform-level accountability | ✗ No managed delivery layer | ✓ Performance management |
| Replacement guarantee | ✓ 14-day free swap | ✗ Dispute process only | ✓ Trial period guarantee | No (rehire cost: 50-200% of salary) |
| IT-only specialisation | ✓ Software & IT engineers only | ✗ General marketplace (all categories) | Partial (covers non-tech roles too) | Hire any role |
Honest take: Toptal has strong vetting and a proven track record. If budget is unlimited and you can wait 2+ weeks for a match, they are a solid option. We win on speed (72hrs vs 5-14 days), pricing transparency (flat 15% vs opaque markup), and managed delivery accountability. In-house is best for core, long-term roles - freelance is faster and cheaper for project-scoped AI work.
From a two-person startup shipping its first LLM feature to an enterprise deploying compliance-grade AI - our managed model scales with you.
Ship your first AI feature fast. We match you with a senior ML engineer who can move at startup speed - no enterprise bureaucracy.
Productionise your ML models and build the MLOps infrastructure to support rapid growth. Managed delivery keeps your roadmap on track.
Deploy AI at scale with full compliance - GDPR, EU AI Act, SOC 2. Our enterprise-grade managed delivery includes model output accountability.
Add AI capabilities to client projects without hiring a permanent ML team. White-label delivery with OTF-managed accountability.
Here are three real-world brief examples that result in fast, accurate matches. The more specific your brief, the better your shortlist.
Sample Brief
Build a retrieval-augmented generation system over 50K internal documents. Stack: LangChain, Pinecone, GPT-4o, FastAPI backend.
Sample Brief
Fine-tune Llama-3-8B on 10K annotated legal documents using LoRA. Deliverable: production model + evaluation framework + deployment pipeline.
Sample Brief
Design and implement end-to-end MLOps pipeline on AWS SageMaker: experiment tracking (MLflow), feature store (Feast), automated retraining, monitoring.
Everything a CTO, founder, or VP of Product typically asks before posting an AI/ML brief.
Before You Hire
Written by our engineering team - no fluff, just the frameworks that work.
Developer rates by region, hidden cost breakdown, TCO comparison across DIY vs managed platforms, and a complete budgeting guide.
The 7-day vetting framework, technical interview questions, take-home test design, and red flags - built for non-technical founders.
Stage-by-stage decision framework for founders and CTOs. Cost, speed, and risk tradeoffs - with a clear verdict for each company type.
Post your brief in 5 minutes. Get 3 live-coded, AI-matched engineers in 72 hours. Full managed delivery - no CV fraud, no bidding chaos.
$0 matching fee · 72 hrs · 14-day guarantee