To hire MLOps engineers in India well, screen for people who have kept models healthy in production, not people who can list tools. The skills that matter are reproducible training pipelines, a disciplined model registry, reliable serving and monitoring that catches drift before customers do. Real experience shows up in incident stories, rollback plans and trade-offs they can explain.
If your models are stuck in notebooks right now, tell us how they are trained and deployed today and we will suggest the smallest step towards a proper pipeline.
MLOps is not DevOps with a new label
A DevOps engineer ships code whose behaviour is fixed by the code. An MLOps engineer ships models whose behaviour depends on data that keeps changing. That adds three problems DevOps rarely faces: reproducing a model from a specific data snapshot, detecting when live data no longer looks like training data, and deciding when a retrained model is safe to promote. It is also not data science. The MLOps engineer does not need to invent the model, but must make sure it can be rebuilt, audited, served and replaced. Hiring for the wrong one of these three roles is the most common and expensive mistake.
Core skills to screen when you hire MLOps engineers in India
Pipelines and reproducibility
- Orchestration with tools such as Airflow, Kubeflow Pipelines, Prefect, SageMaker Pipelines or Vertex AI Pipelines.
- Data and dataset versioning (for example DVC, lakeFS or versioned tables in Delta or Iceberg).
- Containerised, parameterised training jobs that produce the same model from the same inputs.
- Consistent feature logic between training and serving; our note on feature store implementation explains why this matters.
Model registry and promotion
A registry such as MLflow, or the one built into your cloud platform, should record every model version with its data snapshot, code commit, metrics and approval status. Ask candidates how they gate promotion from staging to production: automated evaluation thresholds, comparison with the current champion, and human sign-off for high-risk models.
Serving and infrastructure
- Batch scoring versus real-time APIs, and when each makes sense.
- Serving frameworks such as KServe, BentoML, Triton or managed endpoints.
- Kubernetes, autoscaling, GPU scheduling and infrastructure as code with Terraform.
- Canary, shadow and blue-green rollouts for models, with instant rollback.
Monitoring and drift
Operational metrics (latency, errors, throughput) are only half of it. A strong engineer also monitors input distributions, prediction distributions and, once labels arrive, actual accuracy. They know tools such as Evidently or WhyLabs, and more importantly they know which alerts are worth waking someone up for.
Monitoring for LLM systems
If you run LLM applications, add tracing of prompts and retrieved context, eval suites that run on every change, and cost tracking per request. Many teams now expect their MLOps hire to cover this "LLMOps" layer too.
Signals of real production experience
Resumes in this field often read alike. These questions separate people who have operated systems from people who have only set them up:
- "Tell me about a model that degraded silently." Look for how it was noticed, how long it took, and what monitoring they added afterwards.
- "How would you rebuild the model that was in production six months ago?" The answer should name exact artefacts: data snapshot, commit, environment image and registry entry.
- "A retrained model scores better offline but business metrics fall after release. What happened?" Good answers mention training-serving skew, label leakage, or a shift in the population being scored.
- "How do you keep GPU spend under control?" Expect spot instances, right-sizing, batching, scheduled shutdowns and per-team cost visibility.
Red flags
- Every answer is a tool name with no reason behind the choice.
- No mention of rollback, ever.
- Monitoring means only CPU and memory graphs.
- They have built platforms nobody used, and cannot explain why.
Hiring, contracting or partnering
For a first MLOps hire, context matters. If you have one or two models, a full platform team is overkill; a senior engineer or a partner can set up a lean pipeline, registry and monitoring on your existing cloud. If you have many models and teams, you need a platform owner in-house, possibly supported by specialists during the build. Our guide to MLOps consulting in India compares these paths in more depth.
If you recruit directly, remember that notice periods in India are often long, so a strong candidate may take a couple of months to join. Public GitHub repositories, conference talks and write-ups about real incidents tell you more than certifications. Use a paid, time-boxed exercise on your own stack rather than a generic coding test, and involve the data scientists who will depend on this person in the final round.
iJurug Soft is a Bangalore studio, founded in 2018, and MLOps and cloud engineering sit alongside our AI and machine learning services on AWS, GCP, Azure and Kubernetes. Every engagement is staffed with senior engineers and runs on fixed milestones (Discover, Design, Build, Launch and grow), with long-term support. We can build the pipeline and hand it over with runbooks, or keep running it with your team.
Frequently asked questions
Which cloud should an MLOps engineer know?
The one you run on. Concepts transfer, but managed services differ enough that hands-on experience with your provider shortens the ramp-up considerably.
Can one person cover data engineering and MLOps?
In a small team, often yes for a while. As data volume and model count grow, split the roles so pipelines and model operations each get proper attention.
Do we need Kubernetes for MLOps?
Not necessarily. Managed services like SageMaker or Vertex AI cover many needs. Kubernetes becomes attractive when you want portability, many models or tight control over GPUs.
How is an MLOps engagement quoted?
We do not publish rates. Number of models, cloud setup, compliance, team size and support expectations drive the quote; share your scope and we will reply with a clear proposal.
Want production-grade MLOps without a long hiring cycle? Write to info@ijurugsoft.com or outline your current setup through the contact form. A senior engineer will review it and propose a first milestone you can measure.