Built for people who work Monday to Saturday
The AlgoProfessor Advanced Certificate in AI to Agentic AI Scientist moves in one direction: from machine-learning fundamentals, through large language models and retrieval, to autonomous agents that plan, call tools, coordinate with other agents and run in production.
Every Sunday follows the same three steps: Fundamental, Application and Production. The production step adds that week's skill to AP-Nexus, a single enterprise agentic system that you build across all twelve weeks and release on GitHub as v0.1, v0.2 and so on until v1.0 on Demo Day.
Teaching is led by Dr S Satyanarayana and delivered with his Ph.D. and post-doctoral scholars and with practitioners who build agentic systems in industry today. Responsible autonomy, evaluation and AI ethics are taught as engineering requirements, not as an appendix.
What makes the Sunday format work
Faculty-led sessions every Sunday with real-time doubt clearing. No pre-recorded lecture dumps.
A single enterprise agentic system grows every Sunday, from v0.1 to v1.0, on your own GitHub.
Ph.D. and post-doctoral scholars of the Programme Director mentor small learner pods.
Practitioners from product and services companies run live case-study segments.
Fundamental, Application and Production in one sitting, so no skill stays theoretical.
Résumé, portfolio and mock-interview support, plus referrals to hiring partners.
Who should apply
- Graduates or diploma holders in engineering, science, computer applications, mathematics or statistics
- Working professionals, faculty members, research scholars and final-year students
- Basic Python ability (variables, loops, functions). A free Python primer is shared on selection
- A laptop with 8 GB RAM or more and a stable internet connection
A typical Sunday
- 10:00 – 11:30
- Step 1 · Fundamental
- 11:30 – 11:45
- Break
- 11:45 – 13:15
- Step 2 · Application lab
- 13:15 – 13:45
- Lunch
- 13:45 – 14:45
- Step 3 · Production sprint
- 14:45 – 15:00
- git push · tag · review
Every Sunday: understand it, apply it, ship it
The same three steps run every week, so each new skill moves from theory to a working lab to production code in your project repository on the same day.
Fundamental
10:00 – 11:30 · 90 minConcepts, mathematics and architecture behind the week's skill, taught with live derivations and a short demo. Closed with a 10-minute concept quiz.
Output: concept quiz score
Application
11:45 – 13:15 · 90 minA guided lab on a real dataset or API. You apply the concept in a notebook, compare variants and measure results with your scholar mentor.
Output: graded lab notebook
Production
13:45 – 15:00 · 75 minYou turn the lab into tested, documented code inside your 12-week project, then commit, tag and push the week's release to GitHub.
Output: tagged GitHub release
Twelve Sundays, three phases
Open any week to see its Fundamental, Application and Production steps and the release you push to GitHub that day.
The list of topics, projects and tools is indicative and may be revised by the Programme Director to keep pace with the field.
One page per Sunday
Every week has a one-page sheet: objectives, the 3-step plan, key terms, tools, self-check questions, homework and the GitHub deliverable. Open a week to read it here, or download all 12 sheets as one printable PDF.
Open all 12 sheets (PDF, A4) →AP-Nexus: one project, built one Sunday at a time
Every learner builds the same enterprise system: an agentic research and operations copilot that reads a company's documents and tickets, answers questions, takes actions through tools and escalates to humans. Each week's skill becomes one layer of it and one tagged release in your own GitHub repository.
Your repository is public by default so recruiters can follow the commit history. Private repositories with mentor access are allowed on request.
By the last Sunday you will be able to
Reason about AI systems end to end
Explain how models learn, where they fail, and how an agent observes, reasons, acts and learns.
Use LLMs as reasoning engines
Apply structured prompting, ReAct, reflection and planning to get dependable behaviour.
Ground agents in knowledge
Build retrieval pipelines with vector databases, re-ranking and long-term memory.
Design single- and multi-agent systems
Coordinate role-based agents with LangGraph, CrewAI and AutoGen, and connect tools over MCP.
Ship to production
Containerise, deploy, monitor and evaluate agents with cost, latency and quality metrics.
Build responsible autonomy
Apply guardrails, human-in-the-loop controls, red-teaming and AI-ethics review to every system you ship.
The working stack
Dr S Satyanarayana, Ph.D., PDF (AI)
Ph.D. & post-doctoral scholars
Dr SSN's research scholars teach lab segments and mentor learner pods on projects, papers and code reviews.
Practitioner faculty
Dr SSN's former students now building AI and agentic systems at leading companies run live case-study and deployment sessions.
Mentor-to-learner pods
Every learner is placed in a small pod with a named scholar mentor for the full twelve weeks.
From portfolio to offer
Career support runs alongside the course from Week 6 and continues for six months after Demo Day.
Profile audit
Résumé, LinkedIn and GitHub reviewed against Agentic AI and LLM Engineer job descriptions.
Portfolio polish
Your 12 weekly builds and capstone packaged as a public portfolio with write-ups.
Interview prep
Technical and system-design mock interviews on GenAI and agentic AI topics with industry faculty.
Referrals
Shortlisted profiles shared with AlgoProfessor's hiring partners and the alumni network.
Research track
Strong capstones mentored toward a paper or patent, and guidance for MS / Ph.D. applications.
AlgoProfessor provides placement assistance, not a job guarantee. Outcomes depend on the learner's performance, attendance and the hiring market.
Where an Agentic AI Scientist goes next
The roles below are the ones the 12 weeks prepare you for. Salary figures are published market ranges for 2026, not AlgoProfessor offers. Switch between India and USA to compare.
AI engineer salary ladder
Source: IIT Kanpur E&ICT Academy, AI Engineer Salary in India 2026Jobs this programme prepares you for
The proper way: from first Sunday to first offer
- Weeks 1–6 · Build the baseML, deep learning, LLMs, prompting and RAG. Releases v0.1 to v0.6 show recruiters you can ship data and GenAI code.
- Week 6 · Career track startsRésumé, LinkedIn and GitHub audit against real Agentic AI and LLM Engineer job descriptions.
- Weeks 7–11 · Become agenticMCP tools, multi-agent crews, memory, evaluation, deployment and safety. These are the skills that separate an Agentic AI Engineer from a generic developer.
- Week 12 · Demo DayPresent AP-Nexus v1.0 live to faculty and industry panel. Your certificate links to the release.
- Months 1–3 after · Interview & referMock technical and system-design interviews, then referrals to hiring partners for junior Agentic AI, GenAI and LLM roles.
- Years 1–5 · Specialise & growMove toward RAG and AI product, AgentOps, AI architecture, or the research track (papers, patents, MS / Ph.D.).
Salaries vary with city, company, prior experience, interview performance and the hiring market. AlgoProfessor provides placement assistance, not a job or salary guarantee. Sources: IIT Kanpur E&ICT Academy (India, 2026) · IIT Kharagpur Online, Agentic AI Engineer Salary · KORE1 LLM Engineer Salary Guide (USA).
Your score updates every Sunday
Marks follow the 3-step method. Production work carries the most weight because it is what employers look at. Move the sliders and toggle weekly releases to see how a result is calculated.
Every certificate carries a unique ID for online verification and links to the learner's GitHub v1.0 release. Badges appear on the certificate and the learner's portfolio page. The simulator is illustrative; official marks are entered by faculty each week.
Certificate format
Every learner receives an e-certificate with a unique ID that employers can verify online. It names the grade earned, the GitHub v1.0 release of the 12-week project and the Programme Director's signature.
Certificate of Successful Completion
With Distinction
PROFESSOR
2026
Programme Director
Certificate of Participation
Participation
PROFESSOR
2026
Programme Director
- Learners who score 60% or more overall with at least 75% attendance receive a Certificate of Successful Completion, graded Distinction (85+), Merit (70–84) or Completion (60–69).
- Learners who score below 60% or attend fewer than 75% of Sundays receive a Certificate of Participation.
- This programme is organised by AlgoProfessor AI R&D Solutions, Hyderabad.
Certificate images are illustrative and may be updated by AlgoProfessor. Only e-certificates issued by AlgoProfessor are valid. Open the certificate format (PDF) →
Fee structure
₹1,00,000 + 18% GST · India
US$2,000 USA & international
| Instalment | Due | India | USA / international |
|---|---|---|---|
| Instalment 1 · Seat block | Within 5 days of selection | ₹20,000 + GST | US$400 |
| Instalment 2 | Before 15 Nov 2026 (Week 1) | ₹40,000 + GST | US$800 |
| Instalment 3 | Before 20 Dec 2026 (Week 6) | ₹40,000 + GST | US$800 |
Notes
- GST at the prevailing rate applies to Indian learners. USA and international fees are quoted in US$ and paid online.
- Group discount for 3 or more learners from one organisation or institution.
- Payment links are shared after selection.
Refund policy
- Withdrawal within 14 days of the start date: 80% of the fee received is refunded, excluding taxes.
- No refund after the first two Sundays.
- Email cto@algoprofessor.com to withdraw. Refunds are processed within 30 working days.
Before you apply
Do I need a background in AI or machine learning?
No. Weeks 1 to 3 cover the AI and ML foundations the rest of the course depends on. You need basic Python; a primer is shared on selection.
How much time do I need each week?
Five hours live every Sunday, plus about two hours during the week to finish your module project and the quiz.
What if I miss a Sunday?
Every session is recorded and shared the same evening. Your pod mentor helps you catch up on the lab. Missed releases can be pushed by the following Saturday for half marks.
Who teaches the sessions?
Dr S Satyanarayana leads the programme. His Ph.D. and post-doctoral scholars run lab segments and mentor pods, and industry practitioners run case-study and deployment sessions.
Will I need paid API keys?
No. Labs work with free-tier keys and open models running locally through Ollama. Paid keys are optional.
What is the 12-week project?
AP-Nexus, an agentic research and operations copilot for an enterprise. Every learner builds it in their own GitHub repository. Each Sunday's production step adds one layer and one tagged release, so the commit history shows your progress to recruiters.
How is my score calculated?
Concept quizzes 10%, application labs 15%, weekly GitHub releases 30% (2.5 marks each), capstone and Demo Day 30%, code reviews and participation 15%. Attendance of 75% is required for any certificate above Participation.
Is placement guaranteed?
No. We provide structured placement assistance: profile audit, portfolio, mock interviews and referrals. Hiring decisions rest with employers.
Can faculty members and research scholars join?
Yes. A research track helps learners turn their capstone into a paper or patent draft under the Programme Director's guidance.
Apply for the Sunday Programme
Six short steps, about ten minutes. Fields marked * are required. Your draft is kept in this browser until you submit.
Prefer paper? Open the printable application form (PDF) →Have a question first?
Send a short note and the programme office will reply within two working days.
cto@algoprofessor.comWeb: www.algoprofessor.com