Pre-vetted MLOps engineers for MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI and Kubernetes, ready to join your team within 48 hours.
Stack we work in
Azure Machine LearningFigures as of Oct 2026
Trusted by
Our MLOps engineers take models from notebooks to production and keep them working.
They set up experiment tracking, training pipelines, model registries, serving and monitoring, and they add CI/CD and infrastructure as code around them. They also run LLM workloads with tools such as vLLM and Langfuse. They work in your cloud, repositories and time zone, and you start with a 15-day free trial.
Data scientists build models that work on a laptop, but nobody has the skills or time to put them into production.
The role needs machine learning, cloud, Kubernetes and CI/CD experience together, and a permanent hire can take months.
Without monitoring, drift and data issues are found only after results or revenue drop.
Each model is released differently, so releases are slow, hard to repeat and hard to roll back.
Outcomes
Pick one profile or combine several. Each developer works in your tools, repositories and process.
Builds the shared platform your data scientists train and deploy on.
Packages models and serves them with predictable latency.
Runs training and serving on your cloud's managed ML service.
Watches models in production and triggers fixes.
Runs large language model applications reliably and within budget.
Turning a notebook model into a tested, versioned service with a repeatable release process.
Experiment tracking, registry, pipelines and serving set up on your cloud or Kubernetes.
Automated training, testing and deployment on every approved change.
Drift, data quality, latency and cost dashboards with alerts and retraining triggers.
Shared, versioned features with Feast so training and serving use the same data.
Serving, tracing, evaluation and cost control for LLM and RAG systems.
Making model training repeatable.
Built with
Getting models in front of users.
Built with
Knowing when a model stops working.
Built with
Running ML and LLM systems like software.
Built with
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We review your requirements, platform and goals, and recommend the MLOps engineer roles and seniority your project needs.
You receive Role and skills plan
We shortlist pre-vetted MLOps engineers who passed our technical evaluation, communication check and team-fit review, and who match your stack and time zone.
You receive Developer profiles
You interview the shortlisted developers and choose. Nothing starts until you are happy.
You receive Your chosen developers
Your developers join your tools, repositories and ceremonies within 48 hours of selection. The first 15 days are a free trial.
You receive Developers working in your workflow
The team delivers in agile sprints with daily progress reports, and you scale the team up or down as needs change.
You receive Daily reports and sprint releases
Tech stack
AI and machine learning
Azure Machine LearningData platforms
Pipelines and orchestration
Backend and APIs
Languages
Cloud and DevOps
AWSMicrosoft AzureOther tools
Hiring models
Pick the commitment that fits your roadmap and change it as your needs change. Every model works in your time zone, with clear pricing agreed after a call and no hidden costs.
An MLOps engineer who works only on your project, full working days, as part of your team. Billed monthly, with a 15-day free trial.
Half-day availability for steady MLOps work that does not need a full seat. Billed monthly, and you can move to full-time at any time.
Pay for the hours used, for fixes, audits, small enhancements and short spikes of work. Hours are reported daily, with no long-term commitment.
A fixed scope, timeline and price, delivered by a team we manage end to end, with milestone payments.
A cross-functional squad of developers, QA, a designer and a project manager or scrum master, sized to your roadmap, with one point of contact.
More roles
Hire data, AI, web and mobile developers alongside your MLOps engineers, or build a dedicated team around your roadmap.
Free trial
Interview the shortlisted developers, then judge the work in your real project for 15 days before you commit.
It depends on the seniority, the platforms involved and the hiring model: full-time, part-time, hourly or project-based. After a short call we share profiles and a clear monthly or hourly rate, with no hidden costs, and the first 15 days are a free trial. As a guide, our data and MLOps engineers range from $30 to $70 per hour (USD), depending on seniority and certifications, and full-time monthly engagements cost less per hour than hourly work. We confirm your exact rate after a short call.
It depends on the role, seniority and hiring model. Full-time and part-time developers are billed monthly, hourly work is billed for the hours used, and project-based work is paid in milestones. After a short call we share developer profiles and a clear monthly or hourly rate, with no hidden costs.
Pre-vetted developers can join your team within 48 hours of you selecting them.
Yes. We align developers to your preferred time zone and working hours so you can collaborate in real time.
The first 15 days are free. Use them to assess the developer's skills, code quality, communication, delivery and fit with your process. If you are satisfied, you continue; if not, you can stop with no obligation.
We sign an NDA, use data encryption, secure storage and regular security audits, and follow GDPR requirements. Equitysoft is ISO 27001 certified. You own all code, data and IP, with no lock-in.
Tell us and we will replace them quickly, so your project stays on track.
You set the priorities and direct the work, just as you would with your own staff. You get daily progress reports and talk to the developer through your usual email, video, chat and project tools. We take care of employment, equipment, attendance, performance support and replacements.
Each candidate passes a technical evaluation with practical tasks and code review on the stack you need, such as Python, MLflow, Docker, Kubernetes, Terraform or your cloud ML platform, not just a CV. We also check spoken and written English, how clearly they explain technical decisions, and their experience with agile ceremonies, code review and documentation. You then interview the shortlist and choose.
A data scientist builds and evaluates the model. An MLOps engineer builds the pipelines, infrastructure and monitoring that train, deploy and run that model reliably in production. Many teams hire both.
AWS with SageMaker, Azure with Azure Machine Learning, Google Cloud with Vertex AI, and Databricks. They can also run open-source tools such as MLflow, Kubeflow and KServe on your own Kubernetes clusters.
Yes. They set up model serving with vLLM or a managed endpoint, trace and evaluate prompts and responses with Langfuse, and add cost, latency and quality monitoring.
Share your models, cloud and timeline. We will propose pre-vetted MLOps engineers you can start trialing within 48 hours.
