Your advantage is that you already handle correctness at scale. Extend it by building features that are computed once and read both in training and at inference. Doing this exposes training and serving skew, point in time correctness and backfill problems, which are exactly the failure modes ML system design rounds probe.
You do not need research-level mathematics, but you cannot be vague either. Evaluation metrics and why accuracy misleads on imbalanced data, overfitting, validation splits with time series, and how a model degrades. We drill these against a problem you build, so answers come from your own results rather than a course summary.
Training is the short part. Packaging, versioning, deploying behind an API, controlling latency, logging predictions and detecting drift is the job. We scope one small model, deploy it, and deliberately break it so you can talk about rollback and monitoring from experience rather than from an architecture diagram.
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For applied ML engineering roles, no. Research scientist positions are a different market with different entry requirements. ML engineering hiring is closer to backend and platform work, and teams often prefer someone who can keep a pipeline reliable over someone who can derive a loss function. Where the degree matters most is in getting past screening at a few specific labs and research groups.
There is heavy overlap and the titles are used inconsistently in Indian job descriptions. MLOps roles usually centre on platform, tooling and deployment infrastructure. ML engineer roles typically expect that plus the ability to reason about the model itself, including evaluation and feature design. From a data engineering base, MLOps is the shorter jump, and it is a reasonable stepping stone if the modelling gap is currently wide.
Most of it, and more than candidates assume. Feature engineering at scale, orchestration, data quality and lineage are the substance of an ML platform. What does not carry over is the assumption that a pipeline is finished when the data lands. In ML, correctness is only established once the model consuming the features behaves the same in production as in training.
Internal opportunity is stronger evidence when it exists, because production constraints are real. If your organisation has no ML workload, a focused personal project still works, provided it is deployed and monitored rather than being a notebook with a good score. One deployed model you can discuss honestly, including what went wrong, is worth more than several polished but unused repositories.
