Role hiring
Hire Data, MLOps & LLMOps Engineers
The engineers who keep data flowing and models running in production. This is often the scarcest layer of an AI team.
What the role is
Data engineers build and maintain the pipelines and warehouses that feed analytics and models. MLOps engineers deploy, monitor and retrain models. LLMOps engineers do the equivalent for LLM applications: prompt and version management, evaluation, cost and latency tracking, and safety controls.
Teams that hire modellers first and platform engineers later usually stall at deployment. We help you sequence the hires.
- Data Engineer
- Analytics Engineer
- MLOps Engineer
- LLMOps Engineer
- ML Platform Engineer
- DataOps Engineer
How we screen
What we check before a profile reaches you.
Pipeline design
Batch and streaming, orchestration, data quality checks and what happens when a job fails at 2am.
Production ML
Model serving, monitoring, drift detection and retraining workflows.
LLM operations
Evaluation harnesses, prompt versioning, cost and latency control, and observability.
Cloud and infrastructure
Hands-on depth with the cloud stack your team uses, and infrastructure as code.
How we get you from role to hire
Four steps, one accountable team, and a guarantee after the offer.
Intake
We map the role, tech stack, seniority, budget and must-haves with your hiring manager.
AI screen, expert interview
AI screening filters the pool in seconds. Experienced recruiters and technical leads interview whoever passes.
Shortlist
3 to 5 interview-ready profiles, typically within 48 hours for core tech stacks.
Offer and guarantee
We coordinate interviews, offer and joining. Permanent hires carry a 90-day replacement guarantee.
Common questions
- Should we hire a data engineer or an ML engineer first?
- If your data is not reliable, start with a data engineer. If data is solid and you need a model in production, start with an ML engineer who has deployment experience. We help you decide in the first call.
- What does LLMOps cover?
- Running LLM applications reliably: evaluation, versioning of prompts and models, cost and latency tracking, monitoring for quality and safety issues.
- Can these roles be placed in a GCC?
- Yes. Platform and data teams are a common first hire for a Global Capability Center, and we can build the team inside a Build-Operate-Transfer centre.
Other roles we hire
- Forward Deployed Engineers
Engineers who embed with your customers or product teams and ship working software.
- AI, GenAI & ML Engineers
Machine learning, LLM, RAG and agent engineers, screened on what they have built.
Hiring Data, MLOps & LLMOps Engineers?
Tell us the role and the stack. We will send a shortlist plan within 48 hours.