Hire AI/ML Engineers
Add senior AI/ML engineering capacity vetted for real production experience in model development, deployment, and MLOps.
Add senior AI/ML engineering capacity to your team — vetted for real production experience in model development, deployment, and MLOps, not just coursework or side projects.
What Does an AI/ML Engineer Do?
An AI/ML engineer designs, builds, trains, and deploys machine learning models that solve real business problems, bridging the gap between data science research and production software engineering. Unlike a pure data scientist, an AI/ML engineer is also responsible for making sure the model runs reliably in production — APIs, scaling, monitoring, and retraining included.
Core Responsibilities
Model Development & Training
Design and train machine learning and deep learning models suited to your data and business problem.
Feature Engineering & Data Prep
Transform raw data into the signals a model can actually learn from, in collaboration with data engineers.
Generative AI & LLM Integration
Fine-tune models and build RAG pipelines for teams adopting generative AI features.
Production Deployment
Package models into APIs and services that integrate cleanly with your existing product.
Monitoring & Retraining
Set up drift detection and retraining pipelines so accuracy holds up after launch.
Need an AI/ML engineer who's shipped models to production before?
Get a Free Quote →From Requirement to Onboarded Engineer
Requirement
Tell us the seniority, tech stack, and project context you need an AI/ML engineer for.
Shortlist
We present pre-vetted candidates with verified production ML experience within days.
Interview
You interview and select the engineer that fits your team and project best.
Onboard
The engineer integrates with your codebase, data access, and existing workflows.
Scale
Extend the engagement or add more engineers as your roadmap grows.
What an Augmented AI/ML Engineer Delivers
| Project Type | What They Deliver |
|---|---|
| Predictive analytics initiative | A trained, validated, and deployed forecasting or scoring model |
| Recommendation engine | A tuned recommendation system integrated into your product |
| Fraud or anomaly detection | A real-time scoring model wired into your transaction pipeline |
| Generative AI feature | A RAG-grounded or fine-tuned model integrated into your application |
Hiring Directly vs. Staff Augmentation
Hiring Directly
- Weeks to months of recruiting and screening
- Hard to verify real production ML experience
- Full salary, benefits, and equipment overhead
- Difficult to scale down if priorities shift
Avtrix Staff Augmentation
- Pre-vetted shortlist within days
- Verified production experience, not just theory
- Pay only for the engagement you need
- Scale up or down as your roadmap changes
Hiring AI/ML Engineers FAQs
What seniority levels are available?
We provide mid-level to senior AI/ML engineers, matched to the complexity of your project and the level of independent ownership you need.
How quickly can an AI/ML engineer start?
Most requirements receive a shortlist of pre-vetted candidates within a few business days, with onboarding shortly after you select someone.
Do they work remotely or on-site?
Engagements are typically remote with working-hours overlap matched to your team, though on-site arrangements can be discussed for specific needs.
Can we start with a trial period?
Yes. Our hourly and monthly engagement models let you validate fit before committing to a longer-term arrangement.
What if we need more than one engineer?
We can scale a dedicated team of AI/ML engineers alongside data engineers, MLOps, and other roles as your project grows.
Is confidentiality covered for our data and models?
Yes. NDAs are available on request before any detailed engagement begins.
Ready to Add AI/ML Engineering Capacity?
Tell us the skills and seniority you need, and we'll send a shortlist within days.