Hire Dedicated AI/ML Engineers
Hire AI/ML Engineers Who Get Models Into Production, Not Demos
Most AI projects do not fail on the model. They fail on the data around it, the evaluation nobody wrote, and the bill nobody forecast. Stallyons is a Delaware-registered US company whose senior engineers ship models into production on hours you set.
Triple Protection Guarantee
- US-Registered Entity
- Signed IP Assignment
- Senior ML Engineers
Triple Protection Guarantee:
- US-Registered Entity
- Signed IP Assignment
- Senior ML Engineers
Engineers On Staff
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Avg. Engineer Exp.
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Years In Business
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Why AI Teams Stall
Models In Prod
Not A Demo
Senior Engineers
Vetted Only
LLM & RAG Builds
Grounded AI
Eval Harness
Before Launch
Data Handled
Pipelines
Cost & Latency
Both Budgeted
MLOps Discipline
Monitoring
Communication Rhythm
Daily Sync
Human Review
In The Loop
IP Assignment
Signed
Delivery Overlap
Fixed
Hours
Models & IP Owner
You
Trusted By Startups
What Hiring AI/ML Engineers Actually Means
AI/ML engineer covers three different jobs. One integrates a hosted model behind an API and spends most of the work on retrieval, prompts, evaluation and cost. One builds or fine-tunes models, which needs data and a reason a hosted model cannot serve. One runs the platform: pipelines, feature stores, serving, monitoring and retraining. Hiring goes wrong when a brief says AI and the work is one of these while the engineer has only done another.
Stallyons is registered in Delaware as a US company, and our senior engineers work inside a time-zone window you set, with four-plus hours of daily overlap. You sign a US contract, run diligence on a US entity and pay one US invoice. The engineers on your account average four-plus years of production work across LLM features, data pipelines, applied machine learning and MLOps, backed by twelve-plus years of delivery across six continents, for founders, CTOs and product leaders worldwide.
What AI/ML Engineers Cover
Integration before invention: most products need a hosted model, good retrieval and a strong evaluation set rather than a trained one. We say so when that is true, and build the case for training when it is not.
Data a model can actually be fed: ingestion, cleaning, schemas and feature storage that stay stable, because a model trained on data nobody can reproduce is a result you cannot repeat later.
Evaluation you can argue with: a scored test set, tracked on every change, so improvement is a number rather than an impression formed in a demo where the questions were chosen kindly.
Hallucination controls that hold: retrieval grounded in your own content, citations on answers, refusal when confidence is low, and human review where a mistake is expensive.
Deployment that behaves like software: versioned models, staged rollout, rollback, and monitoring that watches quality and drift rather than only whether the service replied.
Cost and latency designed in: the model sized to the task, caching and batching where answers repeat, and a cost per request tracked from week one rather than found on the invoice.
Why Product Teams Choose Dedicated AI And ML Engineers
- Decide first whether you need integration, applied ML or training. They share a job title and very little else, and few engineers are strong at all three.
- Ask how they would measure success before building anything. If there is no test set and no baseline, you are buying opinions about a demo, not a system.
- Ask what a request will cost and how they know. Anyone who has run this in production quotes tokens, caching and a latency budget unprompted.
- Ask what happens when the model is confidently wrong. You want retrieval, citations, refusal thresholds and a human in the loop, not a promise about a better prompt.
- Ask about the data before the model. Where it comes from, who cleans it, how it is versioned, and what happens when the schema changes underneath the pipeline.
- Ask who owns the prompts, weights and evaluation sets. All three should sit in your repositories from day one, so switching providers is work you can do.
How To Hire AI/ML Engineers Without Risk
Every engagement starts with a free 45-minute technical session. No slide deck, no sales script. You bring the use case, the data and the constraints; you leave with a recommended approach, a clear scope and a timeline.
We are selective about new engagements and cap how many we run at once, because the vetting is the product. If we cannot staff your AI work with engineers we would vouch for, we will tell you so.
Why Clients Choose Us
Full
Written IP Transfer
USA
Contract Entity
Yours
Models & Weights
Named
Senior Level
Ready to get a model in front of your real users?
What AI/ML Engineers Build
What You Can Build With AI/ML Engineers
AI work pays off when the use case is narrow, measurable and worth automating. These are the builds we take on most often, each with an agreed scope, an evaluation set and an overlap window with your team.
LLM Product Features
Assistants, copilots, summarisation
In Product
RAG & Retrieval
Embeddings, vector stores, rerank
Cited Answers
Data Pipelines
Ingestion, cleaning, feature stores
Reusable
Custom Model Training
Fine-tuning, classical ML models
Versioned & Served
Evaluation
Test sets, scoring, regression
Before Ship
Inference Services
APIs, batching, caching, GPUs
Low Cost
Agents & Automation
Tools, function calls, workflows
Linked
Guardrails & Safety
Filters, limits, logging, human review
Enforced
MLOps & Deployment
CI/CD, registries, rollout, rollback
Reproducible
Monitoring & Drift
Quality, cost, latency, retraining
Kept Honest
Not sure if you need a model or an API? Let's work it out.
Common Challenges
Why Do AI Projects Stall At The Demo?
Six failure patterns behind almost every AI project that never left the demo. None of them are about the model.
Demo Never Ships
01
A prototype impresses in a meeting and then meets real inputs. There is no evaluation set, no error path and no owner, so the work sits at ninety percent until attention moves elsewhere.
The Data Wasn't Ready
02
The model is the easy part. The data is scattered, undocumented and inconsistent, and half the project turns into the pipeline nobody scoped or budgeted.
No Way To Measure It
03
Without a scored test set, better is whatever the last demo felt like. Changes get argued from anecdotes and regressions ship because nobody saw them.
Hallucinated Facts
04
The system answers confidently and wrongly, with nothing to cite and no threshold to refuse. Users find the first bad answer faster than any internal test did.
The Token Bill Lands
05
Nobody sized the model to the task, cached anything or trimmed context. The feature works, then the monthly inference cost arrives and the business case stops working.
Model Quality Drifts
06
Inputs move, the world changes and accuracy slides while every dashboard stays green. The service is up, the answers are worse, nobody is watching.
Recognise any of these? Let's fix the cause.
Our AI Engineering Work
Six Ways To Hire Our AI/ML Engineers
Six ways to add AI capacity from one accountable vendor. Run one, or run several in parallel under a single contract.
Dedicated AI/ML Engineers
01
Named AI engineers who join your team, your board and your standups, working to your review rules, with a lead who reports into you rather than around you.
LLM & RAG Delivery
02
Assistants, copilots and search built on hosted models: retrieval over your own content, citations on answers, prompt versioning and an evaluation set that gates every change.
Data & Feature Pipelines
03
Ingestion, cleaning, schemas and feature storage built so a model can be retrained on the same data next quarter and get the same result.
Model Training & Evals
04
Fine-tuning and classical models where they earn their place, with baselines, scored test sets and a written comparison against the hosted option.
MLOps And Deployment
05
Serving, containers, registries, staged rollout and rollback, so a model release is an ordinary deployment rather than an event people brace for.
Monitoring & Model Support
06
Quality, drift, latency and cost tracked per request, with alerts on the numbers that matter and a retraining cadence agreed before anything reaches users.
Not sure which AI engagement fits? Let's scope it together.
Why Choose Us
What Makes Hiring AI/ML Engineers Here Different
The details that decide whether an AI feature survives contact with real users and real bills.
A US Legal Entity
01
Stallyons is registered in Delaware. Your contract, your invoice and your legal recourse sit with a US company, not an unknown one.
Integration Or Build
02
We will tell you when a hosted model and good retrieval is the answer, instead of selling you a training project you do not need.
Overlap You Set
03
You choose the hours we share with your working day, and stand-ups, reviews and escalations all happen inside that window.
Measured, Not Guessed
04
Every change is scored against a test set you can read, so improvement is a number your team can check rather than a claim we make.
Cost Controls
05
Cost per request and latency are tracked from the first week, and both are design decisions we agree with you.
One Contract
06
One contract covers the engagement, so procurement, legal and finance each deal with a single named counterparty.
Ready to see how we run an AI engagement here?
Our Process
From First Call To AI/ML Engineers In Six Steps
A hiring process that settles what your AI problem actually is before anyone signs.
Discovery
Understand the data, use case, hours and budget
Scoping
Agree the use case, scope and success bar
Select
Meet and approve the engineers on your account
Contracting
NDA, IP assignment, access and onboarding
Deliver
Work on your board, reviewed on merge
Review & Scale
Regular reviews, retrain and adjust the scope
Want to see how this maps to your roadmap?
Technology Stack
The Full Stack Our AI/ML Engineers Work In
Modern, battle-tested tools across models, data, evaluation, serving and the infrastructure that runs them.
Models & APIs
Next.js / React
Gemini / Llama
Hugging Face Hub
Fine-Tuning
Embeddings
Frameworks & Serving
Python 3.12
TensorFlow / Keras
PyTorch Core
FastAPI
vLLM & Triton Serve
Data & Storage
PostgreSQL Vector
MongoDB Atlas
Pinecone / Weaviate
Airflow DAGs
Feature Stores / dbt
Evaluation & QA
Ragas / DeepEval
Golden Sets
LLM Judges
Prompt Versioning
Latency Budgets
Cloud & MLOps
AWS / GCP / Azure
Docker / K8s
SageMaker, Vertex
MLflow Model Registry
Grafana / Tracing
Industries We Serve
AI/ML Engineers For Industries With Real Constraints
Engineers who already know your data rules, review needs and edge cases spend month one shipping, not asking.
Fintech & Payments
Fraud scoring, docs, underwriting
Healthcare & HealthTech
Triage, clinical notes, imaging
Retail & E-Commerce
Search, recommendations, pricing
EdTech & Learning
Tutoring, grading, content tagging
SaaS & Digital Products
Copilots, support deflection, tags
Logistics & Supply Chain
Forecasting, routing, ETA models
Manufacturing & IoT
Anomaly detection, quality vision
Agencies & Consultancies
White-label AI build capacity
We know your industry. Let's build the AI/ML plan.
How We Compare
Hire AI/ML Engineers vs Agency vs Freelance
An honest look at your three AI hiring options.
| Capability | In-House ML Hire | Freelance Marketplace | Generalist Dev Agency | Stallyons Technologies |
|---|---|---|---|---|
| Evaluation Before Launch | ✕One Person's View | Demo Only | Not Offered | Scored Test Sets |
| Data Pipeline Ownership | Often Manual | ✕Out Of Scope | ✕Basic ETL | Versioned And Reusable |
| Hallucination Controls | Team Dependent | ✕Prompt Only | Not Considered | Retrieval And Citations |
| Cost Per Request | Rarely Tracked | ✕Your Problem | Passed Through | Budgeted And Monitored |
| Production Monitoring | Ad Hoc Dashboards | ✕None | Uptime Only | Quality, Drift, Latency |
| Real Overlap Hours | Same Time Zone | ✕Whenever | ✕Business Hours | Agreed Overlap |
| Contracting Entity | Your Payroll | ✕Platform Terms | Local Entity | US-Registered LLC |
| Models And IP Ownership | Employment Terms | ✕Platform Default | ✕On Request | Signed Before Access |
See the difference for yourself
Complete Engagement
Everything Included When You Hire AI/ML Engineers
From Scoping to Contracting to Delivery, One Vendor
Here's everything included when you hire AI/ML engineers with us:
All-Inclusive AI/ML Delivery: No Hidden Fees, No Surprises.
Every AI/ML engagement includes all eight components above. One contract, one senior team, one predictable cost, and no vendor sprawl.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free AI Readiness Read
A written read on your use case: whether a model is needed, what the data will support, the likely cost per request, and the three things to build first.
Included Free
Delivery Plan & Estimate
A phased delivery plan with scope, milestones, a stack recommendation and a transparent, itemized estimate for the engagement.
Included Free
Free Use-Case Triage
A written triage of your candidate AI use cases, ranked by value, data readiness and risk, so you can put our judgement to the test.
Included Free
Risk-Free Partnership
Our AI/ML Delivery Promise
We stand behind every engagement with commitments that protect your investment.
01
Scope Agreed First
Scope, model, working hours and cost structure are written down and agreed before contracting, so nothing is discovered later.
02
Built to Last
Senior engineers, code review, automated tests, scored evaluation sets, security audits, and clean, documented code you fully own.
03
IP And Access Protected
NDA and IP assignment are signed before access, permissions are scoped per person, and your accounts stay under your control.
Start your AI/ML build with confidence, backed by our Triple Protection Guarantee.
Track Record
Engagements That Ship, Scale, and Compound
500+
Projects Delivered
29+
Service Categories
81%
Repeat Client Rate
4.9 ★
Clutch Rating
"We came to Stallyons after burning two years and four vendors on a multi-platform launch that kept slipping. They scoped it end-to-end — web app, iOS, Android, an AI summarization layer, and a Shopify integration — and shipped it in 22 weeks. One team, one budget, one quality bar. We've handed them three more engagements since."
Mark Sawyer
CEO/Founder
PlatinumLED
"Stallyons rebuilt our customer-facing portal, integrated three legacy systems, shipped an AI document analysis pipeline, and brought our compliance posture to SOC 2 — all under one engagement. The senior engineers on the team have shipped at companies five times our size. It's the best vendor decision we've made in a decade."
Mark Sawyer
CEO/Founder
PlatinumLED
FAQ
Frequently Asked Questions About Hiring AI/ML
An ML engineer makes models work inside a product. That covers integrating a hosted model behind an API, building or fine-tuning one when integration is not enough, moving the data that feeds it, writing the evaluation that proves it works, and running it in production within a latency and cost budget. Research is a different job. So is the backend work around it, though most ML engineers do some of both.
A data scientist answers questions with data: analysis, experiments, and models that prove something is possible. An ML engineer makes that thing run every day for real users, with pipelines, serving, evaluation, monitoring and a cost per request somebody has to justify. A notebook is the natural output of one; a deployed service is the natural output of the other. Hire the scientist to find the answer and the engineer to ship it.
Rates move with seniority, specialism and whether the job is integration, applied ML or training work. A rate quoted before anyone has seen your data is a guess. Your real cost is the seniority mix, how clean the data is, and the inference bill the design commits you to. We scope first, itemize the estimate, and design for a cost per request you agree.
Ask who you are contracting with and under which country’s law. Ask to meet the engineers who will actually do the work, not the ones in the pitch deck. Check when IP assignment gets signed, how access is granted and revoked, what the review standard is, and what happens when someone leaves. A vendor who answers those without hedging is usually the safer buy.
You do, from the first commit. NDA and IP assignment are signed before anyone gets repository or data access, with no licence-back and no shared ownership. Repositories, prompts, trained weights, evaluation sets and documentation live in your own accounts throughout, so there is nothing to hand back later.
Most products do not need a trained model. A hosted model with good retrieval, careful prompting and a strong evaluation set covers a large share of the work for a fraction of the effort. Training or fine-tuning earns its place when you have proprietary data, a narrow task, a latency floor or a cost curve a hosted call cannot meet.
Ground answers in your own content with retrieval, and make the system cite what it used so a wrong answer is traceable. Constrain the output format, refuse rather than guess when confidence is low, and keep a human in the loop where a mistake is expensive. Then measure it with a scored evaluation set run on every change.
The common ones are unclear scope, juniors replacing the seniors you met, no shared working hours, unsigned IP and knowledge disappearing when someone leaves. We address them the same way each time: written scope, named engineers you approve, an agreed overlap window, IP signed before access, and documented handover before anyone rolls off.
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready to Hire AI/ML Engineers Who Ship Models?
Get a free consultation. We'll discuss your use case, recommend a team shape, and send a detailed written proposal.
