Module 01
AI/ML Engineer
Course Introduction & AI Roadmap
What this program covers, who it is for, and the 4-month learning path from SQL to Agentic AI.
A 4-month, project-driven live online program - from SQL fundamentals to multi-agent AI systems in production. Built for aspiring data scientists, AI engineers, and career switchers who want to build real things, not just collect certificates.
Our students work at
Why Learn Data Science with Generative AI?
Graduate ready with deployed projects, not just a degree.
Backend or frontend devs ready to switch into AI & ML roles.
Already use SQL & Excel? Level up to ML & GenAI engineering.
Non-tech professionals planning a complete pivot into AI.
Upskill in evenings & weekends, with full LMS access for revision.
Build AI-first products yourself before you raise a round.
Not simple video playlists — mentor-driven engineering sprints optimized directly for scaling up skills.
Over 24 weeks of live, mentor-led training, you will move from query languages and statistical foundations, through machine learning and deep learning, into the modern AI stack - LLMs, prompt engineering, embeddings, vector databases, RAG, agentic AI with LangChain & LangGraph, and MLOps for deployment.
Sessions are 100% online and live - taught personally by Mr. Koti, a 12+ year industry practitioner. Every week ends with an assignment reviewed and graded by your mentor. Every phase culminates in a portfolio-grade artifact.
Career support continues until you land your first interview call. We help with LinkedIn & Naukri profile building, ATS-friendly resume writing, mock interviews, and corporate etiquette. Full LMS access for 365 days.
Structured systematically. Focused clearly on active deployments.
With 12+ years of industry experience across data engineering, machine learning, and now agentic AI systems, Mr. Koti has shipped models in production at scale and mentored hundreds of engineers into their first ML roles.
Work confidently using industry platforms across core deployment targets.
Review comprehensive lecture formats directly before verifying course access choices.
Explore complete library setups natively on official streaming setups.
Visit KSR Channel Library →Sequential core units structured logically to compile robust engineering outcomes.
Build predictive models and run machine learning experiments.
Design, train, and deploy machine learning systems at scale.
Build agentic workflows and implement intelligent search.
Develop speech recognition and language understanding tools.
Integrate LLM reasoning, RAG, and vector lookups.
Translate database queries into interactive reporting dashboards.
The curriculum was surprisingly updated. We went beyond basic scikit-learn models and actually built multi-agent workflows using LangGraph and CrewAI. Mr. Koti’s method of debugging code live during class really helped me build the confidence to clear Deloitte’s technical rounds.
I came from a completely non-technical operations background. The initial weeks on SQL and Python foundations were slow enough for me to catch up. By the final month, I was deploying RAG pipelines on AWS. The mock interview sessions at KSR were the reason I got hired.
What stood out for me was the capstone review. KSR doesn't just check if the code runs; they audit your GitHub repo, check your docker compose configurations, and make you present it as if you're explaining it to a client. That preparation helped me land my first job.
ATS-friendly resume rewrite, GitHub audit, and capstone project documentation review.
Profile rebuild with recruiter-targeted keywords, headline crafting, and weekly content tips.
Two structured mocks: one technical (live coding + system design), one HR/behavioural.
Curated job openings and direct intros via our network of 200+ hiring-partner companies.
Communication, client handling, and corporate-readiness coaching from senior practitioners.
Slack community, referral pipeline, and ongoing peer support — useful long after placement.
Discuss personalized pacing trajectories, audit project module options, and verify scheduling suitability safely directly.