MLOps Roadmap: Zero to Job-Ready Engineer

Start at Step 1 and go in order. Plan for 10 to 12 months at 10 to 15 hours a week.

• Do not skip Step 2. You cannot operate a model you do not understand.
• Finish each step project before moving on. Projects are your portfolio.
• Learn one tool per category. MLflow, DVC, Docker and GitHub Actions are enough for the first half.
• Job readiness depends on your existing technical foundation, practical project experience and ability to operate and troubleshoot production-style ML systems.
• Salary ranges on this page are indicative Indian market estimates from AmbitionBox self-reported data (September 2026). Actual pay varies by company, location, experience, skills and production ownership. They are not guaranteed starting salaries for roadmap graduates.

You can skim Steps 1 and 2. Start at Step 3 and focus on shipping.

• Spend your time on Docker, CI/CD, serving (Steps 3, 6, 7) and Kubernetes (Step 8).
• Turn one of your own notebooks into a versioned, tested, deployed service. That is your first project.
• Plan for about 5 to 6 months.

You already know containers, pipelines and on-call. What is new is data, models and drift.

• Do Step 2 first to understand training, evaluation and ML fundamentals.
• Skim Steps 1 and 3 if you already know Linux, Python, Docker, cloud and CI/CD.
• Focus on Steps 4, 5, 6 and 7: model versioning, pipelines, serving and continuous training.
• Skim Step 8 if you already run Kubernetes in production.
• Spend serious time on Steps 9 and 10: drift, retraining, incidents and LLMOps.
• Plan for about 5 to 6 months.

Jump to the platform and operations layers.

• Use Step 7 to automate retraining and release gates.
• Use Steps 8 and 9 for Kubernetes serving, drift monitoring and retraining triggers.
• Finish with Step 10 (LLMOps) to cover the fastest-growing part of the job.
• Plan for about 3 to 4 months.

Use the interview pack to find your gaps first, then fix them.

• Start with the interview preparation pack to identify knowledge gaps.
• Review the roadmap steps that cover those gaps (drift, rollbacks, CI/CD for ML, feature stores).
• Then complete the capstone and practise explaining two or three deployed projects end to end.