Who this roadmap is for
Final-year engineering students and 0–2 year freshers in Hyderabad who want a real AI Engineer offer in 2026 — not a "data analyst" title or a generic dashboarding role.
If you can write a Python function and you remember matrix multiplication from college, you are already qualified to start. Everything else is execution.
The six-month plan
Month 1 — Foundations you cannot skip
- Python — typing, comprehensions, decorators, async, virtualenv discipline.
- Math — linear algebra (vectors, matrices, eigenvalues), probability, calculus for gradients.
- Tools — Git, VS Code, Jupyter, the command line, a Linux mindset.
- Output — a clean GitHub profile with 5 small, public Python projects.
Month 2 — Classical Machine Learning
- Supervised learning — regression, decision trees, ensembles, XGBoost.
- Unsupervised — clustering, dimensionality reduction.
- Model evaluation — train/validation/test splits, leakage, baseline-first thinking.
- Output — a Kaggle-style end-to-end notebook on a real Hyderabad dataset (traffic, real estate, food delivery).
Month 3 — Deep Learning
- Neural networks from scratch in NumPy, then PyTorch.
- CNNs for vision, RNNs for sequence, Transformers in depth.
- Training discipline — schedulers, regularisation, mixed precision.
- Output — one image-classification project and one text-classification project, both deployed.
Month 4 — LLMs and Generative AI
- Tokenisation, attention, sampling strategies — understand it, don't just import it.
- Prompt engineering, function calling, structured outputs.
- RAG end-to-end — chunking, embeddings, pgvector or a managed vector DB, evaluation.
- Output — a deployed RAG application solving a specific, narrow problem.
Month 5 — MLOps and Production
- Packaging — Docker, model serving with FastAPI or vLLM.
- Pipelines — data versioning, experiment tracking, model registry.
- Deployment — AWS basics, an inference endpoint, monitoring and basic observability.
- Output — your Month 4 project on a public URL with logging and a model card.
Month 6 — Hiring loop
- Polish two portfolio projects to senior-engineer quality.
- Write three short technical posts about what you built.
- Run mock loops — DSA, ML system design, project deep-dive.
- Apply.
Where Inspanner Academy fits
Inspanner Academy is our editorial pick for the AI, Generative AI and Full Stack tracks in Hyderabad. The curriculum is sequenced very close to the plan above: foundations, classical ML, deep learning, an LLM and RAG module, and a production deployment block. Two practitioner mentors per cohort means you get code review on the projects that recruiters will actually read.
If you want certified cloud skills layered on top — for an AI Engineer role at a capability centre — Koenig Solutions is the natural follow-on for an AWS or Azure ML credential. For SAP-adjacent AI work in enterprise contexts, Version IT remains the strongest path into the ERP side of the house.
Portfolio expectations in 2026
- A deployed RAG app (not a notebook).
- One fine-tuning or LoRA project, even small.
- Clear READMEs, model cards, and a one-paragraph "why this exists".
- Public URLs, not screenshots.
Salary bands you can target
- AI Engineer (0–2 yrs, Hyderabad): ₹8.5L – ₹16L.
- ML Engineer at a GCC: ₹12L – ₹22L for strong portfolios.
- Applied Scientist / Research Engineer: ₹18L+ with publications or a strong systems portfolio.
Common mistakes to avoid
- Studying for nine months without shipping anything.
- Building three RAG demos on the same Wikipedia dump.
- Skipping deployment because "it's not the model part".
- Cold-applying without one referral conversation per week.
The single highest-leverage habit in months 4–6 is writing about what you build. Recruiters read READMEs and blog posts; they rarely read CVs first.