AI & Machine Learning
Most companies have AI models. Few have AI outcomes.
We place engineers who have shipped production AI at scale. The kind that close the gap between what lives in a notebook and what your business actually runs on.
The Deployment Problem
You don't have an AI problem. You have an execution problem.
The investment is made. The use cases are identified. The pilots ran. But production-ready AI - systems that actually change how work gets done and show up in the business results - keeps slipping.
That's not a research failure. It's a deployment failure. And in 2026, over 75% of AI job postings are seeking domain specialists, not generalists, because companies have learned what goes wrong when an engineer who can train a model can't architect the system around it.
Models that work in evaluation never make it to production because no one owns the infrastructure
AI initiatives stall between data science and engineering with no one accountable for the handoff
Agentic systems get designed by engineers who haven't deployed one at scale before
Pilots produce dashboards. The business produces the same outcomes it did before the investment
The Specialist
What an Elios AI/ML specialist actually does
Our AI engineers don't arrive to audit your data or write a strategy document. They embed in your team and build. They understand the full stack: model architecture, MLOps infrastructure, RAG pipelines, agentic orchestration, inference optimization, and the governance layer that keeps it all accountable.
The 2026 distinction isn't who can build a model. It's who can take your specific problem, select the right approach - fine-tuning, retrieval-augmented generation, agent frameworks, or purpose-built architecture - and ship something that runs in production, integrates with your systems, and scales without falling apart.
That's the profile we place. Not a data scientist who has touched LangChain. A production AI engineer who has done this before.
Not a Generalist
What makes this different
Strong on models, weak on production
Production-first engineers who understand the full deployment stack
Builds the pilot, hands off to engineering
Owns it from architecture through deployment
Generalist AI experience
Matched to your specific domain: agentic, RAG, MLOps, fine-tuning
Ramp time measured in months
Domain-matched and contributing in days
Measured by model performance in evaluation
Measured by outcomes in production
How It Works
Most AI initiatives stall between the notebook and production. Ours don't.
Scope
We scope the actual problem: where the model needs to run, what the workflow requires, what production looks like for your team.
Source
Engineers who have shipped production AI at scale. Evaluated against your stack, your data environment, and your deployment requirements.
Deploy
Integrated into your team. Writing production code, not prototypes. Contributing to real systems from week one.
Deliver
AI systems that run in your environment, at your scale, owned by your team.
“Domain practitioners evaluate candidates the way you would.”
How You Bring One On
AI/ML specialists are available across all three Elios engagement models.
Talent on Demand
You know the role: an MLOps engineer, a fine-tuning specialist, an agentic systems architect. We find the right person, pre-vetted against your actual technical requirements, and you manage them directly.
Best when you have the infrastructure to onboard and direct a senior AI engineer.
Embedded Teams
You're building an AI function from scratch or standing up a new capability. We design the team composition - the right mix of ML engineers, data engineers, and infrastructure specialists - and make sure the team is structured to actually deliver.
Best when the problem is bigger than a single hire.
Managed Delivery
You have a defined AI outcome and don't want to manage the team to get there. We scope the work, staff it with the right engineers, and own the result.
Best when you want production AI delivered without the overhead of a consulting engagement.
Not sure which fits? Request a Consultation and we'll tell you straight.
Who This Is For
Who this is for.
Companies Deploying AI Into Existing Operations
The board approved the AI roadmap. The use cases are clear. But your internal team was built to maintain systems, not transform them. You need engineers who specialize in connecting AI to the workflows, data infrastructure, and decision-making processes your business already runs on - and who can do it without a six-month runway.
Companies Building AI-Native Products
You're building something new and every architectural decision made now compounds. You need engineers who have built production agentic systems, not engineers learning on your product. The difference between a team that ships and a team that pilots often comes down to one or two senior engineers who have done this before.
Enterprises with Stalled Initiatives
The models are good. The pilots ran. But the business impact hasn't materialized and the engineering org can't explain why. An Elios AI engineer embeds in the gap between what your data science team built and what production actually requires - and closes it without restarting discovery.
The model is not the product. The system around it is. We place engineers who know the difference.
The model is not the product. The system around it is.
Tell us what you're building. We'll match the right specialist.