Yes — and the nature of the demand has changed. Indian companies have moved past hiring anyone who knows Jenkins; they now want engineers who can own CI/CD, Kubernetes, cloud infrastructure, and observability in production, and they pay a clear premium for people who can handle incidents, not just pipelines. DevOps, SRE, and platform engineering roles consistently show up among the hardest-to-fill openings across product companies, GCCs, and large service firms. The gap isn't the number of candidates — it's the number who have genuinely operated production systems.

Most people enter from one of three backgrounds — software development, sysadmin/IT support, or QA — and the DevOps career path in India usually runs: junior/support or build engineer → DevOps engineer → senior DevOps or platform engineer → SRE, cloud architect, or engineering manager. In the early years, the winning combination is Linux, networking, Git, one cloud (AWS is the most common in India), CI/CD, Docker, and Kubernetes. After 3–5 years, your value comes from depth — reliability at scale, cost optimization, security, and incident leadership. Jumping tools every six months stalls progress; owning systems end-to-end is what moves you up.

A workable sequence: (1) Linux and networking fundamentals, (2) Git and scripting (Bash, then Python), (3) core services of one cloud platform, (4) CI/CD with Jenkins or GitHub Actions, (5) containers with Docker, then Kubernetes, (6) infrastructure as code with Terraform, (7) monitoring with Prometheus and Grafana. Build 2–3 projects that mimic production — versioned, automated, monitored — and document them on GitHub. Three to six focused months beat a year of scattered tutorials. The step most self-learners skip is operating and troubleshooting what they deploy, which is exactly what interviews and the actual job test.

Certifications get you shortlisted, not hired — so sequence them after fundamentals. A sensible DevOps certification roadmap for beginners in India: start with AWS Solutions Architect Associate (or Azure AZ-104 if your target companies run Azure), then move to Kubernetes — CKAD for workload-focused roles, CKA if you lean toward cluster operations and SRE work. HashiCorp Terraform Associate is a strong third. Do them in that order rather than collecting badges; recruiters weigh hands-on project evidence and interview depth far more than a stack of certificates, but the right first certification does open interview calls.

MLOps is the discipline of taking machine learning out of notebooks and running it reliably in production — versioning data and models, automating training and deployment pipelines, monitoring model behaviour after release, and enabling safe retraining and rollbacks. The reason we need it: a model that isn't deployed, monitored, and retrained creates zero business value, and models degrade in ways ordinary software doesn't — data drift, feature skew, silent accuracy decay. Teams without MLOps practices typically ship one model and spend months firefighting it manually; teams with them ship, measure, and improve continuously.

An MLOps engineer spends the day building and maintaining the plumbing around models: pipelines that ingest and validate data, training jobs that run reproducibly, containerized model serving, CI/CD for ML, and dashboards tracking latency, drift, and prediction quality. There's real debugging — a feature that changed upstream, a stuck GPU queue, a model whose accuracy quietly dropped. The role sits between data scientists and platform/DevOps teams, so communication matters as much as code. If you like infrastructure but want it applied to ML systems, an MLOps job is a strong fit.

Approach it from either side — software/DevOps engineers add ML fundamentals and pipeline tooling (MLflow, Kubeflow, Vertex AI or SageMaker, model serving with FastAPI or TorchServe), while data scientists add Docker, Kubernetes, CI/CD, and cloud infrastructure. Portfolio beats coursework: one end-to-end project where you version data, automate training, deploy a model behind an API, and monitor it for drift will outperform ten certificates. Indian product companies, GCCs, and funded startups are actively hiring here, and candidates who can discuss production failures — not just notebooks — stand out fast in an MLOps career path.

Expect four clusters: (1) SLOs, SLIs, and error budgets — define them and explain trade-offs you've made with them; (2) incident management — your role in a real outage, postmortems you've written, blameless culture; (3) hands-on troubleshooting — a failing Kubernetes pod, a latency spike after a deploy, alert fatigue; (4) automation and toil reduction. Scenario prompts like "p99 latency doubled after release — walk me through your triage" are where experienced candidates win or lose. Prepare stories with metrics and trade-offs rather than tool names; that's what separates strong SRE interview answers from memorized definitions.

DevOps is a culture and set of practices — shared ownership between dev and ops, automation, continuous delivery. SRE (Site Reliability Engineering) is a concrete, prescriptive implementation of those ideas, with defined mechanics: SLOs and error budgets, toil budgets, blameless postmortems, on-call rotations, and reliability treated as an engineering discipline with measurable targets. In Indian hiring, titles overlap heavily — many "DevOps engineer" roles are doing SRE work. Practical rule: if a role centres on deployment pipelines and infrastructure provisioning, it's DevOps-leaning; if it centres on reliability targets, incident response, and production health, it's SRE-leaning.

They point in opposite directions. MLOps operationalizes machine learning — pipelines, versioning, deployment, and monitoring of models your business runs. AIOps applies AI/ML to IT operations themselves — anomaly detection across metrics and logs, alert correlation, incident prediction, automated triage and remediation. A quick way to remember: MLOps is ops for ML; AIOps is ML for ops. They genuinely intersect in modern SRE teams — for example, an LLM-powered assistant that triages incidents is AIOps running on MLOps-managed infrastructure. Direction-wise, MLOps leads to ML platform roles, while AIOps points toward intelligent operations and next-generation SRE work.

It's one of the smoother transitions in tech, because your core strength — running reliable production systems — is exactly what ML teams lack. Add Python-for-ML fluency, learn how models are trained and served (the engineering, not the math), then get hands-on with MLflow, model registries, feature stores, vector databases, and GPU-aware Kubernetes. For LLMOps specifically, learn LLM output evaluation, prompt and version management, guardrails, cost and latency monitoring, and RAG pipelines. Rebuild one real ML project end-to-end with production discipline, then target ML platform or infrastructure roles — teams value engineers who treat models as systems, not magic.

Recruiters spend seconds scanning, so structure beats length. Lead with impact statements showing scale and outcomes — "cut deployment time from 45 to 8 minutes," "ran Kubernetes clusters serving X services," "reduced cloud spend by 30%" — not tool lists. Mirror keywords from the job description (CI/CD, Kubernetes, Terraform, AWS) honestly so ATS filters pass you, and link a GitHub with real, documented work. The most common rejection reasons: responsibilities instead of results, ten tools with no depth in any, and zero production or incident evidence. One targeted page-and-a-half tailored to the role beats a generic three-page resume sent everywhere.