Foundations are assessed
Programming, mathematics, statistics, optimisation and software practices are taught because durable AI judgment needs more than tool familiarity.
One-year technical career-transition program
Build AI systems.
Defend every decision.
The Certificate in AI Engineering and Intelligent Systems is a rigorous, 30-credit pathway from foundations to production-aware AI—taught entirely in person on weekends, built around evidence, and designed to create an employer-visible technical portfolio.
1 academic yearTwo intensive semesters
30 creditsAssessed labs, studios and capstone
In-person weekendsFace-to-face classes, labs and reviews
₹4,00,000 + GSTMerit scholarships up to ₹50,000
01Frame the problemStakeholders · constraints · successVALID
02Build the systemData · models · RAG · agentsBUILT
03Evaluate the evidenceAccuracy · failure · cost · latencyTESTED
04Deploy responsiblyAPIs · monitoring · risk controlsREADY
PORTFOLIO READY FOR TECHNICAL DEFENCE
Jointly awarded byPSG iTech and InsightVerge
Program design & deliveryDeep AI capability building
Industry mentorshipPractitioner perspective
The program promise
More than learning AI.
Learn to engineer it.
You will not stop at prompts, notebooks or model accuracy. You will learn how data, APIs, deployment, monitoring, evaluation, governance and user workflows come together in serious AI applications.
Research discipline. Industry outcomes.
Form hypotheses, build baselines, run experiments, interrogate failures and document evidence—then apply that discipline to an organization’s real technology problem. The goal is not an academic paper. It is a working, evaluated system.
Why this program is different
Depth that shows up
in what you can build.
The architecture is deliberately demanding: assessed foundations, face-to-face problem-solving, systems thinking, industry context and a portfolio that must withstand questions.
In-person by design
Faculty teach face to face through classes, code clinics, labs, studios and reviews—creating accountability, immediate feedback and direct access to expert guidance throughout the learning journey.
Systems, not isolated models
Move from data and models to RAG, agents, APIs, containers, cloud, monitoring, security, governance and the realities of operational AI.
Evaluation is an engineering skill
Measure baselines, leakage, robustness, explainability, failure cases, cost and latency—not just whether a demo appears to work once.
Industry-style R&D discipline
Apply research-grade experimentation to firm-defined or industry-relevant challenges—focused on prototypes, evidence, architecture decisions and implementation.
A portfolio that can be defended
Graduate with working code, evaluation evidence, architecture documentation, responsible AI controls, a system card, demo and viva-style technical defence.
A different design choice
Not a content catalogue.
A capability journey.
The distinction is visible in the learning medium, project architecture and evidence expected from every participant.
What many short-form offerings optimise for
What this program is designed for
Flexible content consumption
Cohort accountability through in-person teaching, labs and clinics
A broad tour of tools and trends
Assessed foundations plus current ML, GenAI, RAG and agentic systems
Model training or prompt demonstrations
End-to-end systems: data, models, APIs, deployment, monitoring and governance
Templated notebooks and common datasets
Original problem framing, evaluated prototypes and firm-defined R&D challenges
Course completion as the primary evidence
Repository, evaluation report, system card, demo and technical defence
Delivery format
In-person depth.
A weekend rhythm.
The program is delivered in person on weekends only, combining the accountability of a campus classroom with the practical intensity of labs, studios and technical reviews.
Program rhythmWeekend
only
One academic year · Two semesters · 30 creditsonly
100% in person · weekends only
Built for professionals who need continuity and real contact.
Face-to-face instruction makes difficult concepts, debugging and design trade-offs easier to work through. The weekend schedule preserves weekday work commitments while maintaining a demanding cohort experience.
- Faculty-led classes and tutorials
- Structured coding and AI labs
- Team studios and peer reviews
- Practitioner masterclasses
- Capstone mentor checkpoints
- Viva-style technical defence
30-credit curriculum
From first principles to AI systems.
Two semesters. Ten connected modules. A deliberate progression from coding and model reasoning to GenAI, deployment, governance and capstone.
Programming, Data Wrangling and Software Practices for AI
Python, notebooks, data workflows, API calls, testing, debugging, Git and clean, reproducible code.
Applied Mathematics, Statistics and Optimisation for AI
Linear algebra, probability, inference, gradients, loss functions, metrics, uncertainty and optimisation intuition.
AI Software Lab: Linux, Git, SQL, APIs and Reproducibility
Environment setup, command line, dependency management, SQL, API testing, version control and experiment tracking.
Machine Learning Foundations and Evaluation
Regression, classification, clustering, ensembles, validation, leakage, calibration, explainability and error analysis.
Studio I: Data-to-Model Sprint
Frame a problem, audit data, establish baselines, compare models, interpret errors and present a defensible solution.
Semester 1 total15 credits
Deep Learning, NLP and Multimodal Foundations
Neural networks, training dynamics, CNNs, transformers, embeddings, NLP, vision, multimodality and model limitations.
Generative AI, RAG and Agentic AI Systems
Prompting, embeddings, retrieval, reranking, RAG evaluation, tool calling, workflow orchestration, agents and guardrails.
Data Engineering, Cloud and MLOps Foundations
Data pipelines, storage, APIs, containers, serving, monitoring, cost/latency trade-offs and operational hygiene.
Responsible AI, Security, Privacy and Governance
Fairness, explainability, privacy, prompt injection, leakage, human oversight, documentation and model risk controls.
Applied AI Capstone Studio
Build and validate an AI/GenAI prototype, document architecture and risks, demonstrate evaluation evidence and defend trade-offs.
Semester 2 total15 credits
The applied research differentiator
Industry R&D without the academic detour.
The capstone brings the rigour of research into the rhythm of product and engineering work. Participants investigate an industry-relevant challenge, build competing approaches, measure results and communicate what should happen next.
The output is a prototype and an evidence pack—not a literature review disconnected from deployment reality.
01
Firm-defined challengeBusiness context, technical constraints and success criteria
02
Experiment charterHypotheses, baselines, data plan, risks and milestones
03
Build and iterateModels or AI systems, alternative approaches and mentor checkpoints
04
Evaluate honestlyFailure modes, robustness, cost, latency, safety and limitations
05
Recommend and defendWorking prototype, architecture, evidence and executive-ready decision
Your proof of capability
Graduate with evidence, not just a certificate.
Every major artifact is designed to make your technical growth visible to reviewers, interviewers and project stakeholders.
01 · BUILDWorking AI/GenAI prototypeA usable workflow, not a slide-only concept.
02 · CODEClean repository and READMEReproducible setup, documented decisions and version history.
03 · EVALUATETechnical evidence reportBaselines, metrics, errors, robustness, cost and latency.
04 · GOVERNModel or system cardIntended use, limitations, risks and controls.
05 · EXPLAINArchitecture one-pagerData flow, components, dependencies and operating assumptions.
06 · DEFENDDemo and technical vivaExplain choices, trade-offs and failure modes under questioning.
Industry mentorship perspective
Connect classroom decisions to enterprise reality.
Practitioner-led guest lectures and masterclasses
Industry problem statements for labs and projects
Consulting-style capstone and architecture reviews
Career readiness and interview preparation
Faculty and experts
Taught by people who have built, advised, researched and led.
The faculty pool combines deep AI research, enterprise implementation, consulting, entrepreneurship and academic leadership—built over decades, not assembled around the latest trend.
215+combined years of experience
9leading US and Indian universities
4perspectives: industry, academia, consulting, ventures
Dr. Sridhar Pappu
PhD, University of Texas at El Paso · 30+ years
GenAI · AI Systems · Responsible AI
Dr. Venkata Dakshinamurthy Kolluru
PhD, Carnegie Mellon University · 30+ years
AI Strategy · Governance · Enterprise Adoption
Dr. Anand Jayaraman
PhD, University of Pittsburgh · 30+ years
Cloud · AI Economics · Operating Models
Dr. Venkatesh Sunkad
PhD, University of Colorado Denver · 30+ years
AI Platforms · IoT · MLOps
Dr. Shonraj Ballae Ganeshrao
PhD, SUNY Buffalo · 15+ years
Healthcare AI · Capability Architecture
Dr. Siddhart Srivatsav Rajendran
PhD, University of Nevada, Reno · 15+ years
Deep Learning · Research Methods · Evaluation
Dr. Vinay Chowdary
PhD, UPES · 15+ years
AI Engineering · IoT · Embedded Systems
Dr. Sreerama Murthy
PhD, Johns Hopkins University · 30+ years
AI Architecture · Ventures · Innovation
Srikanth Rachakulla
Doctoral Candidate, Golden Gate University · 20+ years
Enterprise GenAI · RAG · Solution DeliveryFaculty and practitioner assignments may vary by module and cohort. Combined experience is based on the minimum experience stated in the featured profiles.
Career alignment
Roles that need builders,
not just AI users.
The program builds entry and transition readiness for roles where coding, experimentation, system thinking and stakeholder communication come together.
AI/ML Engineer TraineeGenAI Engineer TraineeApplied Data ScientistAI Product EngineerMLOps / LLMOps AssociateAI-enabled Domain Specialist
Program fee and scholarships
A clear investment.
A structured payment plan.
The published program fee covers the one-year, 30-credit learning experience. GST and other applicable statutory taxes are charged separately.
Total program fee
₹4,00,000
plus applicable GST
Scholarships up to ₹50,000Merit-based, using the Entrance Examination and undergraduate academic record.
Application fee: ₹500
Separate from the program fee and payable at application.
Separate from the program fee and payable at application.
Payment schedule
Five stages spread from admission acceptance to Month 4.
| Payment stage | Amount | Timeline |
|---|---|---|
| Admission Confirmation Fee | ₹45,000 + GST | Upon acceptance of the Admission Letter |
| Instalment 1 | ₹1,00,000 + GST | At least 1 week before program start |
| Instalment 2 | ₹1,00,000 + GST | Month 2 |
| Instalment 3 | ₹1,00,000 + GST | Month 3 |
| Final Instalment | ₹55,000 + GST | Month 4 |
| Total Program Fee | ₹4,00,000 + GST |
An awarded scholarship is ordinarily adjusted against the final instalment. The Admission Letter will state the exact scholarship and revised payment obligation.
Who should apply
For serious career transition.
- Non-CS engineering graduates seeking an AI/ML transition
- Early-career professionals, typically with 1–5 years of experience
- Developers, analysts, domain experts and systems engineers
- Graduates from quantitative disciplines willing to code and build
- Learners able to sustain in-person attendance, labs and team projects
Application and admission
ApplySubmit the program application and pay the ₹500 application fee.
Entrance ExaminationComplete the examination designed to assess readiness for the technical pathway.
Admission and Scholarship EvaluationYour undergraduate record and entrance score are reviewed; admission and any scholarship are stated in the Admission Letter.
Confirm Your SeatPay the ₹45,000 + GST Admission Confirmation Fee within the stated timeline.
Program policies
Know the terms before you confirm.
The policy framework covers fees, scholarship decisions, admission confirmation, cancellation, refunds and a limited cohort-deferral option.
Fee and Payment Policy
- ₹4,00,000 + applicable GST
- ₹500 application fee is separate
- Scholarship, if awarded, is ordinarily adjusted against the final instalment
- The Admission Letter states participant-specific payment obligations
View policy summary
The ₹45,000 + GST confirmation payment accepts the admission offer for the identified cohort. Remaining payments follow the published schedule. GST and other statutory taxes are additional. Payments remain subject to the cancellation, refund and deferral policy.
Admissions and Scholarship Policy
- Application does not itself confirm admission
- Entrance Examination is mandatory
- Academic record and entrance score inform the decision
- Scholarship awards up to ₹50,000 are merit-based, not automatic
View policy summary
Successful applicants receive an Admission Letter identifying the cohort, scholarship if any, applicable fee, confirmation payment and timelines. Applicants are responsible for accurate information and should review the weekend in-person format and all policies before payment.
Cancellation, Refund and Deferral Policy
- 50% of the Admission Confirmation Fee and 50% of every other program fee payment made are refundable when an eligible written cancellation is received at least one week before the scheduled start date
- Requests received less than one week before the start date, or after program commencement, are non-refundable
- One deferral may be requested by the end of the first month
- Deferral is to the immediately succeeding cohort, subject to approval and availability
View policy summary
All cancellation and deferral requests must be submitted in writing through the officially communicated email channel. For an eligible cancellation received at least one week before the scheduled program start date, 50% of the ₹45,000 + GST Admission Confirmation Fee and 50% of any other program fee payments made are refundable. Applicable statutory-tax treatment will follow prevailing law. No refund applies when a participant opts for or receives cohort deferral.
The Admission Letter and the final published policies govern participant-specific terms. Management may review, interpret or amend policy terms where required and will communicate applicable changes through official channels.
Questions, answered
Know what you are signing up for.
This is intentionally more demanding than a short executive certificate or recorded bootcamp.
What is the delivery format?
The program is taught entirely in person on weekends only. The academic experience includes face-to-face faculty teaching, labs, studios, practitioner interactions and capstone reviews.
Do I need a computer science degree?
No. The program is specifically designed to support career transition from non-CS engineering and other quantitative backgrounds. You should be comfortable with quantitative reasoning and willing to invest sustained effort in coding.
How is this different from a short AI certificate?
It is a one-year, 30-credit program with assessed foundations, frequent coding labs, two studio experiences, system-level AI engineering, responsible AI controls and a final technical defence.
What does “industry R&D” mean here?
It means applying research-grade experimentation to a real firm-defined technology challenge: formulate hypotheses, test alternatives, analyse failures and produce evidence for an engineering or product decision. It is not positioned as academic publication research.
How do scholarships work?
Merit-based scholarships of up to ₹50,000 may be awarded using the Entrance Examination score and undergraduate academic record. The scholarship is not automatic; the Admission Letter states the approved amount and revised payment obligation.
What happens if I need to withdraw or defer?
If an eligible written cancellation request is received at least one week before the scheduled program start date, 50% of the Admission Confirmation Fee and 50% of every other program fee payment made are refundable. Requests received later, including after the program begins, are non-refundable. Alternatively, a one-time deferral to the immediately succeeding cohort may be requested by the end of the first month, subject to approval and availability; no refund applies when deferral is chosen or approved.
What will I have at the end?
A validated AI/GenAI prototype, clean code repository, technical evaluation report, architecture note, responsible AI checklist, model or system card, demonstration and viva-style technical defence.
Admissions open
Build. Evaluate. Deploy.
Make your AI capability visible.
Request the detailed program structure, weekend delivery schedule, admissions information and current policy documents for the Certificate in AI Engineering and Intelligent Systems.