Hire AI engineers who ship models, not slideware.
Machine learning and LLM engineers who build, fine-tune and deploy AI that works in production, inside your team and your tools.
Why hire AI developers through GloberixAI?
GloberixAI places vetted machine learning and LLM engineers who build, fine-tune and deploy AI that works in production. They work full-time in your time zone, pass five practical rounds and keep your data inside your own environment.
- Availability
- Matched within 48 hours
- Commitment
- Full-time, 40 hours a week, in your time zone
- Skills
- LLMs, RAG, computer vision, MLOps
- Ownership
- 100% of the code is yours, under NDA
Last updated
AI engineers who don’t just call APIs.
They build, fine-tune and deploy custom models, then keep them honest in production.
Beyond API wrappers
Our engineers adapt foundation models such as Llama, Mistral and GPT-class models to your proprietary data, and build custom models when that is the better answer.
Evaluation-first engineering
Every feature ships with test sets, metrics and guardrails, so quality is measured and regressions are caught early.
Production experience
Latency, cost, reliability and monitoring are part of the job, not an afterthought once the demo works.
Privacy by design
Sensitive data stays in your environment, with access controls, redaction and clear data boundaries.
Full-time, in your time zone
Dedicated engineers working 40 hours a week with overlap in your working hours, onboarded within 48 hours.
You own everything
Models, code, prompts, evaluation sets and documentation are committed to your own repositories, under NDA.
The AI talent you can hire.
Our engineers cover the whole machine learning spectrum and can join your product immediately.
Machine learning engineer
Builds and trains predictive models for forecasting, ranking, recommendation and classification, including time-series and behavioral models.
LLM and GenAI engineer
Builds RAG systems, agents and fine-tuned models on open and hosted LLMs, with vector databases and robust ETL behind them.
MLOps engineer
Pipelines, deployment, monitoring and cost control for models running in production at scale.
Computer vision engineer
Detection, segmentation and OCR models, including lightweight models that run on edge devices with low latency.
Data scientist
Experiment design, analysis and modeling that tell you what to build and whether it worked.
AI product engineer
Turns models into reliable product features with APIs, interfaces and evaluation built in.
A loop that catches problems before users do.
Every AI feature our engineers build runs through the same five-stage loop.
Define
Agree what good looks like: the task, the metrics and the failure cases that matter to your business.
Build
Start with the simplest approach and a baseline, then add retrieval, fine-tuning or more complex models only when they earn it.
Evaluate
Test on real examples with automatic checks and human review, tracking quality, latency and cost together.
Guard
Add validation, citations, safety filters and fallbacks, so wrong or risky answers are blocked or handed to a person.
Monitor
Watch quality, drift and spend in production, and feed what you learn back into the next version.
Five rounds before an engineer joins your team.
Every engineer is tested on real machine learning work, not trivia, before we put them in front of you.
Portfolio and publications
We review shipped systems, open-source work and papers. Candidates need production AI experience, not only notebooks.
Applied ML coding test
A timed task on real data: frame the problem, build a baseline, evaluate it and explain the trade-offs.
System design interview
They design an AI feature end to end, from data and retrieval to evaluation, deployment and monitoring.
Evaluation and code review
They review a real pull request and write an evaluation for a model output, including failure cases and guardrails.
Communication round
A mock sprint call covering stand-ups, estimates and written updates, so they fit your team and your hours.
Build your AI team.
Pick a team size and seniority. We review your use case and send vetted profiles that fit, matched within 48 hours.
Dedicated AI engineer
A full-time machine learning or LLM engineer, 40 hours a week, embedded in your team and your time zone.
AI squad with a lead
Two to eight engineers led by a tech lead who owns delivery, evaluation and code quality.
Fixed-scope AI sprint
A defined sprint, such as a RAG proof of value, an evaluation harness or a model deployment.
How many engineers?
Seniority
Builds features independently and reviews teammates’ code.
2 Senior AI engineers
Full-time, 40 hours a week. Matched within 48 hours. You own 100% of the code.
The tools your engineers will use.
| Technology | Used for |
|---|---|
| PyTorch | Training and fine-tuning models |
| Hugging Face | Open models and datasets |
| LangChain and LlamaIndex | Agents and retrieval pipelines |
| Vector databases | pgvector, Pinecone and Qdrant |
| FastAPI | Fast, typed model APIs |
| MLflow | Experiments and model registry |
| Docker and Kubernetes | Scalable model serving |
| AWS, Azure, Google Cloud | Managed AI and GPU infrastructure |
Hiring AI engineers, answered.
Practical answers from the engineers who build AI for a living.
What is the difference between an AI engineer and a traditional software engineer?
A software engineer writes deterministic code. An AI engineer also works with data and probabilistic models, so they design datasets, run experiments, measure quality and manage drift, on top of the usual engineering skills.
Do you build custom models or just use OpenAI wrappers?
Both, depending on what the problem needs. Our engineers fine-tune and deploy open models, train classic machine learning models and build retrieval systems, and they use hosted models where that is the better trade-off.
How do you protect data privacy during model training?
Sensitive data stays in your environment. We use access controls, redaction and private deployments, and configure providers so your data is not used to train public models.
How quickly can your AI developers join our team?
Within 48 hours of confirming the match. They join your Slack, Jira or Linear, and GitHub or GitLab, and start on tickets right away.
Which models and frameworks do your engineers work with?
Hosted models from major providers, open models such as Llama and Mistral, and frameworks including PyTorch, Hugging Face, LangChain and LlamaIndex, plus the MLOps tooling to run them.
Who owns the models, prompts and data?
You do. Models, code, prompts, evaluation sets and documentation are committed to your own repositories, under NDA.
Tell us your use case. We’ll match you in 48 hours.
Share what you want AI to do. We send vetted profiles that fit, and you interview them this week.