Hire IT Talent · MLOps Engineers
Hire MLOps Engineers in India
MLOps and AI infrastructure engineers screened on hands-on deployment work, from model registries to GPU capacity, for contract, contract-to-hire or permanent roles.
- MLflow, Kubeflow & SageMaker
- Contract · C2H · Permanent
- Remote, hybrid & on-site
MLOps Engineer Staffing
Engineers Who Keep Models Healthy After Launch
A model that shines in a notebook is only the start. Once live it needs a repeatable way to reach production, a record of exactly which version is serving customers, and monitoring that notices when the world changes around it. An MLOps engineer builds all of that: CI/CD for ML, model registries, feature stores, monitoring, drift detection and scalable GPU and cloud infrastructure. Zilo AI helps companies across India hire MLOps engineers and AI infrastructure engineers who take models to production and keep them healthy.
Every candidate is screened for hands-on work with real data, modern frameworks and deployment, not just research experience, with tools such as MLflow, Kubeflow, Docker, Kubernetes and SageMaker. Hire on contract to stand up a first deployment path or reduce GPU spend, on contract-to-hire to see how someone works with your data scientists, or on permanent terms when models are core to your product. A dedicated team can cover a wider AI build.
CI/CD for ML
Pipelines that treat models as releases, so that training and promotion are repeatable and do not rely on one person's laptop.
Registries and Feature Stores
Model registries and feature stores that show what is live, where it came from and which features it relied on.
Monitoring and Drift
Monitoring and drift detection that notice a model going stale, including the slow decline that no error log will ever show.
GPU and Cloud Infrastructure
Scalable GPU and cloud infrastructure for training and serving, including AI infrastructure for LLM workloads.
Roles We Fill
MLOps and AI Roles We Fill
Pair an MLOps or AI infrastructure engineer with AI/ML and GenAI engineers who build the models, and an AI architect who sets the overall design.
AI/ML Engineer
Builds, trains and deploys machine learning models for prediction, recommendation, computer vision and classification, with clean feature pipelines, rigorous evaluation and measurable business impact.
Generative AI (GenAI) Engineer
Develops LLM-powered products using retrieval-augmented generation (RAG), fine-tuning, embeddings and evaluation pipelines, and integrates them securely into your applications and workflows.
MLOps / AI Infrastructure Engineer
Takes models to production and keeps them healthy: CI/CD for ML, model registries, feature stores, monitoring, drift detection and scalable GPU and cloud infrastructure.
AI Solutions Architect / AI Architect
Designs scalable, cost-efficient AI architectures, selects the right models and platforms, and aligns AI initiatives with security, compliance and long-term business goals.
For Recruiters & Hiring Managers
How to Hire an MLOps Engineer for Production AI
MLOps ads often read like DevOps with a few AI words added, or like a data scientist who also runs clusters. Neither brings the right person. Start from the models you actually serve, and use these points to scope the job and test for it.
Writing the job post? Start from our MLOps Engineer Job Description, a template you can copy and edit.
Not sure which skills to test for? Our guide to MLOps Skills covers what to look for at each level and how to check it.
Interview questions worth asking
- How would you serve a model with a strict latency target on a limited number of GPUs?
- True labels arrive weeks late. How do you know a model is degrading?
- What would you need to rebuild the exact model that served customers last quarter?
- A model does well offline and badly online. What do you look at first?
- The GPU bill keeps growing. How would you bring it down?
- How would you release a new model version without exposing all users to it?
Separate It From ML Engineering
An ML engineer builds and improves models. An MLOps engineer builds the systems that deploy, version and monitor them. Say which you are hiring, because one person rarely does both deeply.
State What You Serve
An honest MLOps engineer job description says whether you run tabular models on CPUs, deep learning or LLMs on GPUs, since the infrastructure problems differ widely.
Name Your Platform
A SageMaker team and a self-managed Kubeflow team need different experience. List what you run, and say if changing it is under discussion.
Use a Notebook Exercise
Hand over a notebook and a trained model, then ask for a containerised service with a health check and a monitoring plan. Structure matters more than speed.
Ask About GPU Cost
Anyone who has run GPUs in production will talk about utilisation, batching, checkpointing and scaling to zero. Vague answers suggest limited real exposure.
Test the Working Relationship
Data scientists are this engineer's main customers. Include one on the panel to see whether the candidate can explain trade-offs without talking down.
Engagement Models
Flexible Ways to Hire
Choose the model that fits your budget, timeline and team structure, and switch as your needs change.
Contract Staffing
Add vetted professionals for a fixed term or project and scale the team up or down as your roadmap changes.
Best forProject spikes & tight deadlines
Contract-to-Hire
Work with a professional on contract first, then convert them to a full-time employee once you are confident in the fit.
Best forDe-risking critical hires
Permanent Hiring
We source, screen and shortlist candidates for full-time roles on your payroll, and coordinate interviews through to the offer.
Best forBuilding your core team
Dedicated Teams
Get a ready-to-go team of specialists aligned to your goals, working in your tools, processes and time zone.
Best forNew products & long-term programs
How It Works
Hire in Four Simple Steps
A clear, collaborative process that respects your time and gets the right people into your team.
-
1
Request
Share the role, must-have skills, experience level, budget and preferred engagement model with our team.
-
2
Shortlist
We source and technically screen candidates, then share only the profiles that genuinely match your brief.
-
3
Interview
You interview the shortlisted candidates and select the best fit, and we coordinate schedules and feedback.
-
4
Hire
Onboard your new expert and start building immediately, with our team on hand as you scale.
Why Zilo AI
Why Companies Choose Zilo AI for IT Hiring
Hiring technical talent takes time your team doesn't have. We take on sourcing, screening and coordination, so your engineers only spend time with candidates worth meeting.
- Role-specific technical screening before every shortlist
- Coverage across AI, data, cloud, software, security and ERP
- Contract, contract-to-hire and permanent hiring from one partner
- Talent for remote, hybrid and on-site roles across India
- A single point of contact from requirement to onboarding
Industries We Serve
- BFSI & Fintech
- Healthcare & Life Sciences
- E-commerce & Retail
- SaaS & Product Companies
- Telecom
- Manufacturing
- Semiconductors & Electronics
- EdTech
- Logistics & Supply Chain
FAQs
Hiring MLOps Engineers: Common Questions
Have a question we have not answered? Talk to our team and we will get back to you quickly.
Talk to Our Team
- +91 77604 02792
- connect@ziloservices.com
- Mon to Fri: 8am to 10pm
- Hebbal Kempapura, Bangalore
Which MLOps and AI infrastructure roles can Zilo AI help us hire?
We hire MLOps and AI infrastructure engineers who work on CI/CD for ML, model registries, feature stores, monitoring and GPU infrastructure, along with AI/ML, GenAI and AI architect roles.
Do you offer contract MLOps engineer staffing?
Yes. You can hire MLOps engineers on contract to set up a deployment path or cut GPU spend, on contract-to-hire to assess the fit, or on permanent terms.
How do you screen MLOps engineers?
Every candidate is screened for hands-on work with real data, modern frameworks and deployment, which covers tools such as MLflow, Kubeflow, Docker, Kubernetes and SageMaker.
What is the difference between MLOps and DevOps?
MLOps shares DevOps practices such as CI/CD and infrastructure as code, then adds what is unique to models: they depend on data, they degrade without a code change, and they often need GPUs.
Do you treat AI infrastructure and MLOps as one role?
Largely, and many companies use the titles interchangeably. AI infrastructure leans towards GPU clusters and LLM serving, while MLOps covers the wider model lifecycle. We hire for both.
When do we need an MLOps engineer?
Usually when several models are live, or when data scientists are spending much of their time on deployment. With one model and low traffic, an ML engineer may cover it for now.
Ready to Hire MLOps Engineers?
Tell us about the models you run, your platform and your cloud, and get a shortlist of screened MLOps and AI infrastructure engineers.