ML Engineers · Data Engineers · MLOps

Hire ML and MLOps engineers

ML engineers who’ve worked on production AI. Hiring ML talent is slow and expensive, and a strong researcher isn’t always the engineer who can keep a model running in production. Our engineers come from a company that builds, ships and operates its own AI products, so production is the default, not an afterthought.

Tell us the role you need

Roles

The engineers we place

Hire data engineers, ML engineers and MLOps engineers through AI staff augmentation from a company that builds its own AI products. Teams usually hire MLOps engineers once a model has to stay healthy after launch.

ML Engineer

Builds, trains and optimizes models, and turns them into services other systems can call.

Data Engineer

Builds the pipelines that get clean, reliable data to models and analytics, and keeps them running.

MLOps Engineer

Handles deployment, versioning, monitoring and retraining pipelines: the infrastructure that keeps models alive after launch.

AI/ML Full-Stack Engineer

Builds the application around the model: APIs, interfaces and integrations with your existing systems.

How it works

From request to working engineer

1Tell us the role and stack you need.
2Review profiles.
3Interview the engineers yourself. You choose who joins.
4Onboard. They work in your tools, your standups and your process.
5We manage the rest: employment, payroll and equipment.

AI tools

How our engineers use AI tools

Under your rules.: Engineers use only the AI tools your policy approves. If your policy doesn’t allow AI tools on your codebase, they don’t use them.
Human review, every time.: Every AI-assisted change is checked by the engineer who made it and goes through your normal code review. Nothing ships because a tool suggested it.
Ownership.: Code produced for you is yours, however it was written.

Why Aalam

Why engineers from Aalam

They’re supported by a team that builds and runs AI in production.

Need the system built first, not just the team? See AI/ML Development Services →

Need full-stack, .NET or Java developers who work with AI tools? See AI-Enabled Developers →

FAQ

ML engineer questions

What is the difference between an ML engineer and an MLOps engineer?

An ML engineer builds and evaluates the model. An MLOps engineer gets it into production and keeps it there: deployment, monitoring, retraining and the pipeline around it. Most failed AI projects we see have the first and not the second.

Have your ML engineers actually shipped models to production?

Yes. Our most recent ML system went live in July 2026 on a client's own server, has run for three months and has been retrained three times since launch. We can walk you through how its accuracy was measured.

Can they work on our existing models, or only build new ones?

Both. The work we are proudest of on the ML side was rebuilding an existing prediction engine and measuring the old and new versions the same way so the improvement was real rather than flattering.

Do you have data engineers as well?

Yes, and you usually need one first. On most projects the data work is larger than the modeling work. On our last ML engagement, 649 job records came in and 455 were usable after cleaning, with a logged reason for every row removed.

What about cloud ML platforms?

We work on your infrastructure, whatever it is. Our most recent deployment was deliberately on premises with Docker, because the client wanted no cloud and no external services. We do not push a platform because we have a partnership with it.

Tell us the ML role you need

Share the role and the stack. We’ll tell you honestly whether we have the right people.

Tell us the role you need