Hire MLOps Engineers from India
Deploy, scale, and manage machine learning systems with pre-vetted MLOps engineers from India. Hire experienced MLOps engineers to build ML infrastructure, automate model deployment, create ML pipelines, implement model monitoring, and run reliable machine learning systems in production.
- Scalable & Flexible Remote MLOps Teams
- MLOps Profiles In As Fast As 48 Hours
- Time-zone Aligned Delivery
- In-House AI & ML Development Capabilities
Review Candidate
Ready for Interview
Match Score
98%
Trusted tech talent delivery partner for enterprises and conglomerates.
ISO 9001:2015 and ISO 27001:2022 Certified.
MLOps Engineers Ready to Take Machine Learning into Production
Hire pre-vetted MLOps talent with the infrastructure, automation, and machine learning expertise needed to build reliable ML systems.
MLOps engineers help organizations bridge the gap between data science, machine learning development, and production operations. They build the systems and processes required to deploy, monitor, maintain, and scale machine learning models.
Pre-Vetted MLOps Talent
Work with engineers evaluated across machine learning infrastructure, model deployment, automation, cloud platforms, CI/CD, and production ML systems.
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Production-Ready ML Infrastructure
Build reliable pipelines and infrastructure that move machine learning models from experimentation into scalable production environments.
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Flexible Hiring Models
Hire an individual MLOps engineer, a dedicated MLOps team, or specialized ML infrastructure expertise to complement your existing AI and engineering teams.
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Global Remote Collaboration
Work with India-based MLOps engineers who can collaborate with teams across the USA, Europe, Middle East, and other global markets.
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Hire MLOps Engineers with the Right ML Infrastructure Expertise
Find MLOps specialists based on your machine learning architecture, cloud environment, and production requirements.
Building a machine learning model is only one part of the journey. MLOps engineers create the infrastructure, automation, deployment processes, monitoring systems, and workflows required to operate ML models reliably at scale.
BEEU TECH helps you hire MLOps engineers based on your existing technology stack and specific ML infrastructure requirements.
Key MLOps expertise includes:
- Machine learning model deployment
- ML pipelines and workflow automation
- CI/CD and continuous machine learning
- Model monitoring and observability
- Machine learning infrastructure
- Model versioning and experiment tracking
- Cloud-based ML infrastructure
- Docker and Kubernetes for ML workloads
- Model serving and inference systems
- Data and model pipeline automation
- ML platform engineering
- Infrastructure as Code for ML environments
Included: Payroll, Flexible Scale Options, 1 Week Evaluation Period (On request)
Don’t see a role listed? Send the brief. We scope the role before we source, so the shortlist you receive from us matches the actual job.
ML Pipeline Engineers
Design and automate end-to-end machine learning pipelines covering data preparation, model training, validation, deployment, and monitoring.
ML Deployment Engineers
Deploy machine learning models as reliable production services using APIs, model serving platforms, containers, and cloud infrastructure.
ML Platform Engineers
Build reusable platforms and infrastructure that allow data scientists and ML engineers to develop, deploy, and manage models efficiently.
ML Monitoring & Observability Engineers
Implement systems to monitor model performance, data quality, infrastructure health, latency, failures, and model drift.
Cloud MLOps Engineers
Build and manage production ML environments across AWS, Azure, Google Cloud, and cloud-native machine learning platforms.
For prediction and modelling see Data Scientists ⇗
Hire MLOps Engineers the Way Your Business Needs
NDA and DPA on every engagement. IP assigned to you. ISO 27001:2022-certified data security, and the work stays on your infrastructure.
Dedicated MLOps Engineer
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MLOps Development Team
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Project-Based MLOps Development
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Scale Your MLOps Team
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Why Hire MLOps Engineers from BEEU TECH?
Get carefully selected MLOps talent that can help you build reliable machine learning systems without the complexity of hiring and screening from scratch.
Pre-Vetted MLOps Engineers
Matched to Your ML Stack
Flexible & Scalable MLOps Teams
India-Based, Globally Connected
How Much Does It Cost to Hire an MLOps Engineer?
MLOps engineer costs vary based on experience, cloud expertise, ML specialization, infrastructure complexity, and engagement model. Engineers working across Kubernetes, cloud infrastructure, ML platforms, model deployment, and advanced automation may have different cost profiles from general DevOps or software engineers.
Use our calculator to estimate the potential cost of hiring MLOps engineering talent based on your requirements.
- Data backed salary insights
- Complete transparency
- Vetted by talent delivery experts
No signup required. 100% Confidential.
What Can You Build with Our MLOps Engineers?
Build the infrastructure and automation required to operate machine learning reliably at scale.
Automated ML Pipelines
Create repeatable pipelines for data preparation, model training, validation, deployment, and retraining.
Model Deployment Infrastructure
Deploy machine learning models as scalable production services with reliable inference and API infrastructure
ML CI/CD Pipelines
Automate testing, validation, model packaging, deployment, and release processes for machine learning applications.
Model Monitoring & Observability
Monitor model performance, data drift, model drift, infrastructure health, latency, errors, and other production metrics.
Cloud ML Infrastructure
Build scalable machine learning infrastructure using cloud platforms, containers, Kubernetes, and infrastructure automation.
ML Platforms & Automation
Create internal ML platforms that help data scientists and machine learning engineers develop, deploy, and manage models more efficiently.
Built for enterprise & GCC delivery environments. ISO 27001:2022 backed talent deployment.
How BEEU TECH Helps Deliver MLOps Engineering to Real-World Results
“Getting a model into production was taking weeks. Our data science team knew how we wanted to fix it, but we didn’t have the engineering capacity to build the deployment and monitoring.”
— Head of Data Science (name withheld on request)
CASE STUDY
Digital Lending Platform , India
200+ employees
Challenge:
The company had 14 credit models running in production.
A retrained model could take up to nine weeks to reach production because data science had to hand the model to engineering, which then packaged and deployed it manually.
The team also had limited visibility once models were live. Changes in the data could affect model performance without anyone noticing early.
The platform team needed additional MLOps capacity to build the deployment and monitoring work alongside its existing release commitments.
What we did:
→ Added two MLOps Engineers through a dedicated staff augmentation engagement
→ Built a standard deployment path from the model registry to production
→ Moved the four highest-volume models onto it first, then the remaining ten
→ Added model and data monitoring for the live models
→ Set alerts for agreed thresholds and routed them to the data science and model risk teams
→ Worked alongside the existing platform team throughout
62 hrs
More Than MLOps Engineers. Complete AI & Cloud Capabilities.
Build the complete technology foundation behind your machine learning systems.
MLOps sits at the intersection of machine learning, software engineering, DevOps, cloud infrastructure, data engineering, and automation.
BEEU TECH can support the broader capabilities required to build, deploy, integrate, and operate modern AI and machine learning applications.
FAQs About Hiring MLOps Engineers
Understand what MLOps engineers do, what skills they bring, and how they can help your organization move machine learning models into reliable production environments.
What does an MLOps engineer actually do?
An MLOps engineer builds the infrastructure, automation, deployment pipelines, monitoring systems, and operational processes required to run machine learning models reliably in production. Works alongside ML and Data Science teams.
What skills should I look for in an MLOps engineer?
Look for engineers experienced with Python, Docker, Kubernetes, CI/CD tools, and ML platforms like MLflow, SageMaker, or Kubeflow.
How much does it cost to hire MLOps engineers in India?
Rates vary by project scope, but typically range between $35–$90 per hour or $2,500–$4,000 per month for full-time professionals.
Can I hire MLOps engineers for short-term setup or consulting?
Yes. You can hire engineers for one-time setup — like pipeline automation, model deployment, or infrastructure audits.
What industries benefit most from MLOps?
Any business that uses AI models — from finance and healthcare to logistics and SaaS — can benefit from structured MLOps.
Why hire MLOps engineers from India through BEEU Tech?
Because we focus on reliability and integration. Our engineers work as an extension of your data science team, helping you ship faster and scale smarter. Also: Hiring from India can provide access to a broad technology talent pool with expertise across machine learning, cloud infrastructure, DevOps, automation, and software engineering.
What types of MLOps engineers can I hire?
You can hire ML pipeline engineers, ML deployment engineers, ML platform engineers, cloud MLOps engineers, and engineers specializing in model monitoring and ML infrastructure.
What is the difference between DevOps and MLOps?
DevOps focuses primarily on software development and infrastructure operations, while MLOps extends similar principles to machine learning workflows, including model training, validation, deployment, monitoring, and model lifecycle management.
Can MLOps engineers work with cloud platforms?
Yes. MLOps engineers can work with cloud environments such as AWS, Microsoft Azure, and Google Cloud to build scalable machine learning infrastructure.
Can MLOps engineers work with Kubernetes and Docker?
Yes. Containers and Kubernetes are commonly used to package, deploy, scale, and manage machine learning workloads in production environments.
Can MLOps engineers work with our existing ML team?
Yes. MLOps engineers are specifically suited to working alongside data scientists, machine learning engineers, software developers, DevOps teams, and data engineers.
Can MLOps engineers build an ML deployment system from scratch?
Yes. Depending on the requirements, an MLOps engineer can help design ML infrastructure, build pipelines, automate deployment, implement monitoring, establish model versioning, and create production workflows.
Thinking About Hiring MLOps Engineers?
Ones who can help you automate, deploy, monitor, and scale your machine learning systems.
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Need to hire someone other than MLOps Engineers? We’ve got you covered.
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