AI development services, delivered by the top 1% of remote AI engineers
Hyparz AI development services give you pre-vetted remote AI engineers who build machine learning models, LLM applications, RAG systems, AI agents, MLOps pipelines and workflow automation inside your own team. It is for companies that need AI designed, built and shipped without a months-long search and without handing the work to a black-box agency. Hyparz matches engineers typically within days, starts every engagement with a one-week risk-free trial and assigns a dedicated account manager for the life of the project.
Why teams choose Hyparz for this
Engineers in your team, not a black box
An agency sells you a project and keeps the know-how. Hyparz embeds pre-vetted AI engineers in your repository, your tools and your sprint, so the models, prompts, evaluation sets and pipelines stay in your hands. Source-code protection is built into every engagement, and your team keeps the knowledge when the work is done.
Matched in days, proven in a week
Every engineer is vetted before you ever see a profile, so you interview a short list rather than a pipeline and are typically matched within days. A one-week risk-free trial on your real problem confirms the fit. If it is not right, you do not pay for the trial and you keep any work delivered during it.
One network for the whole AI stack
Real AI products need more than one skill: a machine learning engineer for the model, an LLM developer for the product layer, a data engineer for the pipeline and a backend developer to ship it. Hyparz draws all of them from one pre-vetted network, so you can start with one expert and grow to a blended or full remote team.
Hyparz at a glance (October 2026)
Vetting pass rate (AI roles)
About 5% of applicants pass
About 5% of the AI engineers who apply to Hyparz pass vetting, which tests real-world ability, system design and communication on practical tasks.
Indicative hourly rates (USD)
- Mid-level AI engineer: $60 to $100 per hour
- Senior AI / ML engineer: $110 to $190 per hour
- MLOps / AI infrastructure specialist: $120 to $200 per hour
Indicative bands for engineers matched through Hyparz; they track the rates Arc.dev publishes for vetted freelance AI talent. Toptal does not publish rates. Your quote depends on specialism, seniority and engagement length. Sources: Arc, freelance AI developers for hire (October 2026): $60 to $100+ per hour; Arc Employer Blog, freelance developer rates 2026 (updated May 2026): AI/ML engineering $110 to $190, MLOps $120 to $200.
What you get
- A scoped technical plan: which problems need ML, which need an LLM and which need plain software
- Trained or fine-tuned models (LoRA/QLoRA where appropriate) with reproducible training and honest evaluation
- LLM features built on GPT, Claude, Gemini or open-weight models, with structured outputs and tool calling
- Retrieval pipelines (chunking, embeddings, hybrid search, reranking) over your own documents and data
- AI agents with scoped tools, guardrails, human hand-off and an evaluation set that gates every release
- Serving and MLOps: APIs, batch jobs, monitoring, drift and cost dashboards, rollback paths
- Data pipelines and warehouses on Airflow, Databricks or Snowflake feeding the models
- Documentation and handover so your team can run and extend everything without us
How it works
Brief
Tell us what you are building, the stack you run and the outcome you need. We work out which AI disciplines the project actually contains and whether it needs one engineer or a small pod.
Match within days
You receive a short list of pre-vetted engineers whose track record sits in that specific lane. Interview them on your real problem, not on trivia, and pick the one you want to start with.
One-week risk-free trial
The engineer works on your real codebase for a week. You see output, communication and judgment before you commit. Not a fit? You do not pay for the trial and you keep the work.
Scale up or down
Add a second specialist when the bottleneck moves, grow to a dedicated team, or wind down when a phase ends. Your dedicated account manager handles the changes and automated timesheets keep billing transparent.
Where this delivers results
Customer support agents
Agents that resolve tickets end to end: they read the conversation, look up the order or account, take the permitted action and hand off cleanly to a person when confidence is low. Built with tool permissions, evaluation sets and monitoring from day one. See our AI agent development services.
Internal knowledge search and document extraction
RAG systems that answer questions over contracts, policies, tickets and wikis with citations, and extraction pipelines that turn invoices, claims or forms into structured data. Retrieval quality is measured on a test set, never assumed. See RAG implementation services and LLM application development.
Forecasting, scoring and classic ML
Demand forecasting, churn and lead scoring, fraud and anomaly detection, pricing and recommendations. Where the data is structured and the target is clear, gradient-boosted models and purpose-built networks usually beat prompting an LLM, and our machine learning engineers will tell you which applies.
Operations automation and data platforms
Workflows on n8n, Zapier or Make with LLM steps for judgment calls, and the Airflow, Databricks or Snowflake pipelines that feed reporting and models. The unglamorous half of most AI programmes, and the half that decides whether the model ever reaches production. See AI automation services.
What do AI development services cover?
AI development services cover the full path from a business problem to an AI feature running in production: data preparation, model training or selection, LLM application and retrieval design, agent orchestration, serving, monitoring and the automation around it. At Hyparz the work is delivered by pre-vetted remote engineers who join your team, rather than by an agency that owns the project. Hyparz is a remote-talent marketplace that matches companies with pre-vetted remote software and AI engineers, typically within days, with a one-week risk-free trial.
The disciplines we vet for, each mapped to the people who deliver it:
- Machine learning: prediction, ranking, forecasting and anomaly detection on your own data, by machine learning engineers and data scientists.
- LLM applications: products built on GPT, Claude, Gemini or open-weight models such as Llama and Qwen, with prompting, structured outputs, evaluation and cost control. See LLM application development.
- RAG: grounding answers in your documents with chunking, embeddings in pgvector or Qdrant, hybrid retrieval and reranking. See RAG implementation services.
- AI agents: systems that plan, call tools and take actions in your CRM, ERP or ticketing stack, with guardrails and human hand-off. See AI agent development services.
- MLOps: serving with vLLM or managed endpoints, experiment tracking, drift and cost monitoring, by MLOps engineers.
- Data engineering and workflow automation: Airflow, Databricks and Snowflake pipelines plus n8n, Zapier and Make workflows. See AI automation services.
Agency, freelancer or marketplace: which should you choose?
Choose an agency when you want a fixed-scope deliverable and are comfortable with the knowledge living outside your company. Choose a freelancer from a gig platform when the task is small and self-contained and you can vet the person yourself. Choose a vetted marketplace such as Hyparz when AI is becoming part of your product and you want engineers inside your team, already vetted, with a trial before you commit.
The honest trade-offs:
- Agency: strongest when scope is fixed and known. Weakest when the scope evolves, which AI projects almost always do, and when you later need to maintain what was built.
- Gig freelancer: cheapest to start, but you carry the vetting, the continuity risk and the security of your data and keys.
- Hyparz: pre-vetted engineers (about 5% of AI applicants pass vetting) embedded in your team, matched typically within days, a one-week risk-free trial, a dedicated account manager, automated timesheets and source-code protection. You manage the work; we manage the match and the engagement.
Which engagement model fits your project?
Most AI programmes start with one person and grow. Hyparz offers three shapes so you can grow without changing partner.
- Individual expert: best when you have an engineering manager and one clear AI workstream, such as adding an LLM feature or a forecasting model. You manage the engineer directly; start is typically within days.
- Blended team: best when the project needs two or three complementary skills, for example an LLM developer, a data engineer and a backend developer. You set priorities; the team plugs into your sprint; start is typically within days of the first match.
- Full remote team: best when AI is a product line rather than a feature and you want a complete pod with its own lead. See dedicated AI team for how that is run.
What drives the cost of AI development?
The cost of an AI project is driven far more by scope decisions than by hourly rates. The five drivers we see most often: how much autonomy the system needs (a classifier is cheaper than an agent that takes actions), how many systems it must integrate with, how high the accuracy bar is and how it will be measured, how ready your data is, and what compliance or review the output requires. Each of these moves the number of engineer-weeks far more than the choice of model.
It is worth getting this right. According to McKinsey, The state of AI in 2025 (November 2025), 88 percent of surveyed organisations report regular AI use in at least one business function, yet only about 6 percent qualify as AI high performers, the group that reports redesigning workflows and scaling rather than piloting. The gap is delivery, not access to models. For market rate ranges by role and seniority, read our guide to the cost of hiring AI engineers and how to hire AI engineers. Weighing Hyparz against Toptal, Turing or Arc? See the platform comparison and Hyparz vs Toptal.
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