Professional Certificate Programme — Genvarsity
Flagship Programme · PCP
Cohort 2026 Open

Professional
Certificate
Programme

A rigorous 6-month practitioner curriculum built for the AI-first economy, 3 months Course; 3-month supervised industry internship. A structured transition from learner to working professional.

AI-First Curriculum
Design Thinking Led
5 Industry Tracks
Live Internship
Scout Express Placement
GCC-Aligned Outcomes
Apply — ₹60,000 Explore Syllabus
Programme Summary
₹60,000
Total programme fee · All-inclusive
Duration6 months total
Course phase3 months live
Internship phase3 months embedded
ModeOnline + Industry site
CredentialProfessional Certificate
PlacementScout Express network
Stipend eligibilityHost company determined
Secure Your Seat →
Cohort size is capped. Admissions are rolling.
5
Industry Tracks
9
Months Total
100+
Hiring Partners
GCC
Placement Pipeline
AI
First Curriculum
Design Philosophy

Built to create builders and thought leaders.

The Indian professional education market produces graduates who know what things are called. Genvarsity produces professionals who understand how things work — and what to do when they don't.

The PCP curriculum is architect-led: every module is designed backwards from a real industry deliverable. Theory is instrumental. Practice is primary. AI tools are infrastructure, not novelty.

Every track integrates a mandatory AI Fluency core — not because AI is a trend, but because the professionals hired by GCCs, new-age enterprises, and product companies in 2026 and beyond are expected to use AI as a default working tool.

Curriculum Principles
01

AI-First, Not AI-Adjacent

Every track operationalises AI tools from week one. LLMs, automation, and AI-assisted workflows are woven into each module, not appended as electives.

02

Design Thinking as Operating System

Problem framing precedes solution building. Human-centred problem decomposition is the backbone of every project and capstone across all tracks.

03

Deliverable-Driven Learning

Each module culminates in a professional-grade artefact — a working model, an audit report, a deployed application, a strategic brief. Certificates reflect completed work, not attended hours.

04

Industry Immersion, Not Simulation

The 3-month internship is an embedded placement in a host company — not a case study exercise. Learners operate within real teams, against real deliverables, under real accountability.

05

Thought Leadership, Not Rote Certification

Curriculum is calibrated to produce professionals who can hold a strategic conversation, not merely execute an assigned workflow. Communication, systems thinking, and professional judgement are taught explicitly.

Track 01 · AI / Machine Learning

AI/ML &
Generative AI
Engineering

The most rigorous applied AI/ML programme in Genvarsity's portfolio. Designed for engineering graduates and technically inclined learners, this track produces professionals capable of building, deploying, and evaluating production AI systems — not merely consuming them. From classical ML to large language model fine-tuning, the curriculum maps to what GCCs, product companies, and AI-native firms actually hire for.

Programme Outcomes

  • Build and deploy end-to-end ML pipelines from raw data to production API
  • Design and evaluate RAG systems, LLM agents, and fine-tuned models
  • Execute MLOps workflows with experiment tracking and model governance
  • Apply AI solutions to real domain problems using structured problem decomposition
  • Hold technical and strategic conversations with AI product teams
  • Placement-ready for AI/ML Engineer, Data Scientist, and GenAI roles in GCCs
Phase 1 · Months 1–2 · AI & ML Foundations
MODULE 01
Weeks 1–3
AI Fluency & The Modern AI Stack
  • How LLMs work: tokens, attention, transformers — conceptually rigorous, no hand-waving
  • The AI tooling landscape: Claude, GPT-4o, Gemini, Mistral — capability mapping
  • Prompt engineering: zero-shot, few-shot, chain-of-thought, system prompting, output constraints
  • AI-assisted workflows: code generation, documentation, research synthesis, analysis
  • Responsible AI: hallucination mechanics, bias sources, organisational risk frameworks
  • Design Thinking introduction: problem framing, empathy mapping, HMW statements
AI Tools Bootcamp
MODULE 02
Weeks 4–6
Python for Data & ML Engineering
  • Python data structures, file I/O, exception handling, functional patterns
  • NumPy: array operations, broadcasting, vectorisation, linear algebra foundations
  • Pandas: data manipulation, merging, groupby, time series, data quality checks
  • Matplotlib & Seaborn: exploratory visualisation, statistical plots
  • Scikit-learn API structure: estimator interface, pipelines, preprocessing
  • Jupyter, Git, virtual environments — professional development workflow
Project: End-to-end EDA on a real dataset
MODULE 03
Weeks 7–9
Classical Machine Learning: Theory to Practice
  • Supervised learning: regression (linear, ridge, lasso, polynomial), classification (logistic, KNN, SVM, decision trees)
  • Ensemble methods: random forests, gradient boosting, XGBoost, LightGBM — tuning and interpretation
  • Unsupervised learning: K-means, DBSCAN, hierarchical clustering, PCA, dimensionality reduction
  • Feature engineering: encoding, scaling, imputation, feature selection, interaction terms
  • Evaluation: bias-variance trade-off, cross-validation, confusion matrices, ROC-AUC, calibration
  • Hyperparameter optimisation: grid search, random search, Optuna
Project: Predictive model on tabular business data
MODULE 04
Weeks 10–12
Deep Learning Foundations
  • Neural network fundamentals: perceptrons, activation functions, forward/backpropagation
  • PyTorch: tensors, autograd, nn.Module, training loops, GPU acceleration
  • CNNs: convolution, pooling, transfer learning with ResNet/EfficientNet
  • Sequence models: RNNs, LSTMs, the vanishing gradient problem
  • Transformer architecture deep dive: multi-head attention, positional encoding, encoder-decoder
  • Training practice: batch normalisation, dropout, learning rate scheduling, early stopping
Project: Image classification with transfer learning
Course · Months 1–3 · 12 Weeks
MODULE 05
Weeks 10–12
Retrieval-Augmented Generation (RAG) — Production Grade
  • Embeddings: dense vs. sparse, embedding model selection (OpenAI, Cohere, sentence-transformers)
  • Vector databases: Chroma, Pinecone, Weaviate — indexing strategies, metadata filtering
  • RAG pipeline architecture: document loading, chunking strategies (fixed, semantic, hierarchical, recursive)
  • Retrieval quality: hybrid search (BM25 + dense), reranking (Cohere Rerank, cross-encoders)
  • RAG evaluation: RAGAS framework — faithfulness, answer relevance, context precision
  • Production considerations: latency, caching, cost management, observability
Project: Domain-specific RAG system with evaluation dashboard
MODULE 06
Weeks 10–12
LLM Fine-Tuning & Model Customisation
  • When to fine-tune vs. prompt engineer vs. RAG — decision framework
  • Parameter-efficient fine-tuning: LoRA, QLoRA — theory and implementation
  • Dataset preparation: instruction formatting (Alpaca, ChatML), quality filtering, deduplication
  • Fine-tuning with Hugging Face Trainer: SFTTrainer, PEFT, model checkpointing
  • Quantisation: INT8, INT4, GGUF, GPTQ — model compression for deployment
  • Evaluation of fine-tuned models: benchmark selection, human eval, automated metrics
Project: Fine-tuned domain-specific instruction model
MODULE 07
Weeks 10–12
AI Agents & Agentic System Design
  • Agentic architectures: ReAct, Plan-and-Solve, Reflection patterns
  • Tool use and function calling: Anthropic tool_use, OpenAI function calling, schema design
  • LangChain agents: agent executor, custom tools, memory modules
  • LangGraph: stateful multi-step workflows, conditional branching, human-in-the-loop
  • Multi-agent systems: orchestrator-subagent patterns, agent communication protocols
  • Agent reliability: prompt injection risks, tool failure handling, output validation
Project: Multi-tool autonomous agent for a business workflow
MODULE 08
Weeks 10–12
MLOps & Production AI Systems
  • Experiment tracking: MLflow — logging parameters, metrics, artefacts, model registry
  • Model serving: FastAPI-based inference endpoints, model versioning, A/B testing
  • Containerisation: Docker fundamentals, Docker Compose for ML services
  • Monitoring in production: data drift detection, model performance degradation, alerting
  • CI/CD for ML: GitHub Actions, automated testing, deployment pipelines
  • Cost governance: token usage tracking, inference optimisation, infrastructure sizing
Capstone: End-to-end deployed AI application with monitoring
Claude API
LangChain
LangGraph
Hugging Face
PyTorch
Pinecone
MLflow
FastAPI
Docker
RAGAS
Jupyter
GitHub
Industry Internship Phase · Months 4–6
Embedded · Live Work

Placement

Embedded in an AI/ML or data team at a GCC, product company, or AI-native organisation via Scout Express.

Deliverables

Real project ownership — model development, pipeline optimisation, or agent deployment. Weekly deliverable documentation.

Credential

Professional Certificate in AI/ML Engineering, Scout Express profile activation, LinkedIn endorsement, and internship letter.

Track 02 · Full Stack Web Development

AI-Augmented
Full Stack
Engineering

Modern web development is no longer about writing every line from scratch — it is about architecting systems, making sound engineering decisions, and leveraging AI tools to accelerate delivery without losing ownership or understanding. This track produces engineers who can build, ship, and maintain production-grade full stack applications using the current industry stack, with AI as a core part of their development workflow.

Programme Outcomes

  • Architect and deploy full stack applications end-to-end on cloud infrastructure
  • Build performant React frontends with modern patterns and state management
  • Design and implement REST and GraphQL APIs with Node.js/Python backends
  • Integrate LLM APIs and AI capabilities into production web applications
  • Apply CI/CD, containerisation, and observability practices from day one
  • Placement-ready for full stack, frontend, and software engineer roles
Phase 1 · Months 1–2 · Engineering Foundations
MODULE 01
Weeks 1–3
AI-First Developer Workflow & Web Fundamentals
  • AI-assisted development: GitHub Copilot, Claude for code, Cursor — using AI without losing ownership
  • HTML5 semantic structure, accessibility patterns, SEO fundamentals
  • CSS3: box model, flexbox, CSS Grid, custom properties, responsive design, dark mode
  • JavaScript ES2023+: closures, prototypes, async/await, modules, destructuring
  • Git & GitHub: branching strategies, pull requests, code review practices
  • Design Thinking: user journey mapping, wireframing, prototype-first development
Project: Responsive portfolio site with accessibility audit
MODULE 02
Weeks 4–6
React & Modern Frontend Architecture
  • React fundamentals: JSX, component composition, props, lifting state, controlled forms
  • React hooks: useState, useEffect, useReducer, useContext, custom hooks
  • State management: React Query for server state, Zustand for client state
  • Next.js: App Router, server components, SSR vs SSG vs ISR, API routes, middleware
  • TypeScript: type inference, interfaces, generics, utility types, React + TypeScript patterns
  • Component testing: Vitest, React Testing Library, mock strategies
Project: Full CRUD app with Next.js App Router
MODULE 03
Weeks 7–9
Backend Engineering: Node.js & API Design
  • Node.js runtime: event loop, streams, buffers, cluster, worker threads
  • Express.js: routing, middleware chains, error handling, rate limiting, CORS
  • REST API design: resource naming, HTTP semantics, versioning, pagination, HATEOAS principles
  • GraphQL: schema definition, resolvers, dataloaders, subscriptions, Apollo Server
  • Authentication: JWT, OAuth2/OIDC, session management, refresh token rotation
  • API documentation: OpenAPI 3.1, Swagger, automated contract testing
Project: Production REST API with authentication and docs
MODULE 04
Weeks 10–12
Databases: Relational, NoSQL & Vector Storage
  • PostgreSQL: schema design, normalisation, indexes, EXPLAIN ANALYZE, query optimisation
  • ORMs: Prisma schema-first development, migrations, relationships, raw queries
  • MongoDB: document modelling, aggregation pipeline, indexing strategies, Atlas Search
  • Redis: caching strategies, session storage, pub/sub, rate limiting patterns
  • Vector databases for web apps: pgvector with PostgreSQL, semantic search in applications
  • Database security: parameterised queries, least privilege, encryption at rest
Project: Multi-database backend with caching layer
Phase 2 · Months 3–4 · AI Integration & Production Engineering
MODULE 05
Weeks 10–12
Integrating AI & LLMs into Web Applications
  • Anthropic Claude API, OpenAI API — streaming responses, tool use, structured outputs
  • AI SDK for Next.js: streaming UI, useChat, useCompletion, server actions
  • Building AI features: semantic search, document Q&A, content generation, classification endpoints
  • Prompt management in production: versioning, A/B testing prompts, monitoring quality
  • Cost and rate limit management: token budgeting, caching LLM responses, fallback strategies
  • AI safety in web apps: input validation, output sanitisation, content filtering
Project: AI-powered web application with streaming UI
MODULE 06
Weeks 10–12
Cloud, DevOps & Infrastructure
  • Docker: multi-stage builds, compose, networking, secrets management
  • Cloud deployment: Vercel (frontend), Railway/Render (backend), AWS fundamentals (EC2, S3, RDS, CloudFront)
  • CI/CD: GitHub Actions — test pipelines, build optimisation, deployment automation
  • Observability: Sentry for error tracking, PostHog for product analytics, uptime monitoring
  • Performance: Core Web Vitals, code splitting, lazy loading, CDN strategy, image optimisation
  • Environment management: secrets, configuration, multi-environment deployment
Project: Production deployment with full CI/CD and monitoring
MODULE 07
Weeks 10–12
Web Security & Engineering at Scale
  • OWASP Top 10: injection, XSS, CSRF, IDOR, SSRF — prevention patterns in Node.js
  • Security headers, CSP, HTTPS, HSTS, cookie security attributes
  • Microservices patterns: service decomposition, API gateway, inter-service communication
  • Event-driven architecture: message queues (BullMQ, RabbitMQ), webhooks, event sourcing basics
  • Horizontal scaling: load balancing, stateless design, distributed session management
  • Technical debt management, code review culture, documentation standards
Security audit + architecture review of prior project
MODULE 08
Weeks 10–12
Capstone: Full Stack Product Build
  • Requirement gathering and system design document — practitioner-grade
  • End-to-end build: React/Next.js frontend, Node.js or Python backend, PostgreSQL, cloud deployment
  • Minimum one AI feature integrated — semantic search, LLM assistant, or AI content pipeline
  • CI/CD pipeline with automated tests, staging environment, production deployment
  • Code review sessions with practitioner mentors
  • Product demo, architecture walkthrough, and engineering decisions justification
Portfolio Capstone Project
Claude API
GitHub Copilot
Cursor
React
Next.js
TypeScript
Node.js
PostgreSQL
Prisma
Docker
AWS
Vercel
GitHub Actions
Industry Internship Phase · Months 4–6
Embedded · Live Work

Placement

Placed as a junior full stack or frontend engineer in a product company, GCC, or technology startup via Scout Express.

Deliverables

Feature ownership within a live codebase, sprint participation, code review processes, documented pull requests and releases.

Credential

Professional Certificate in Full Stack Engineering, Scout Express placement profile, GitHub activity record, and internship letter.

Track 03 · Cybersecurity

Cybersecurity &
Ethical Hacking
Professional

A rigorous, practitioner-designed cybersecurity programme aligned to the EC-Council CEH curriculum and extended with AI-era threat intelligence, cloud security, and red team/blue team operations. This track does not produce checkbox certificate holders — it produces security professionals who understand how attacks work, how defences fail, and how to build organisations that can withstand both. AI is integrated throughout as both a threat vector and a defensive tool.

Programme Outcomes

  • Perform structured ethical hacking engagements across network, web, and cloud environments
  • Conduct vulnerability assessments, penetration testing, and security audits
  • Design and implement defensive security architectures and incident response playbooks
  • Understand AI-powered attack vectors and AI-assisted threat detection
  • Aligned to CEH v13, CompTIA Security+, and industry SOC analyst competency frameworks
  • Placement-ready for security analyst, SOC, pentesting, and GCC security roles
Course · Months 1–3 · 12 Weeks · CEH Aligned
MODULE 01
Weeks 1–3 · CEH 01–06
Foundations, Reconnaissance & System Hacking
  • Cybersecurity fundamentals: CIA triad, MITRE ATT&CK framework, kill chain, threat taxonomy
  • Networking for attackers: OSI/TCP-IP stack, DNS, HTTP/S, TLS, routing and switching mechanics
  • Footprinting & OSINT: Shodan, Maltego, Google dorking, WHOIS, passive and active recon
  • Active scanning: Nmap port/service/OS detection, Nessus vulnerability scanning, network mapping
  • System hacking: password attacks (Mimikatz, pass-the-hash), Metasploit exploitation framework
  • Privilege escalation: Windows (UAC bypass, DLL hijacking), Linux (SUID, cron jobs, PATH abuse)
Lab: Full recon report + controlled exploitation on isolated Metasploitable environment
MODULE 02
Weeks 4–6 · CEH 07–11
Web Application & Network Attack Vectors
  • OWASP Top 10 (2023): injection, XSS, CSRF, IDOR, SSRF, broken access control in depth
  • Web app pentesting: Burp Suite Professional — interception, scanner, intruder, repeater
  • SQL injection: manual and automated (SQLmap), blind, time-based, out-of-band techniques
  • Authentication attacks: session hijacking, JWT vulnerabilities, OAuth2 misconfigurations
  • Sniffing & MitM: Wireshark, ARP poisoning, traffic analysis, de-authentication attacks
  • Social engineering: phishing, spear phishing, vishing, AI-generated deepfake threat landscape
Lab: Full penetration test on DVWA and OWASP WebGoat
MODULE 03
Weeks 7–9 · CEH 12–17
Malware Analysis, Cloud Security & Cryptography
  • Malware taxonomy: viruses, trojans, RATs, ransomware, fileless malware, bootkits
  • Static & dynamic analysis in sandboxed VM: PE headers, strings, Process Monitor, Wireshark
  • AV evasion: obfuscation, packing, AMSI bypass, in-memory execution concepts
  • Cloud attack surface: AWS IAM escalation, misconfigured S3, metadata SSRF, Pacu framework
  • Container security: Docker escape, Kubernetes RBAC misconfigurations, Trivy image scanning
  • Cryptography: AES, RSA, ECC, TLS mechanics, PKI infrastructure, common implementation flaws
Lab: Malware analysis report + AWS sandbox misconfiguration audit
MODULE 04
Weeks 10–12
SOC Operations, Incident Response & Capstone
  • SIEM: Splunk fundamentals — data ingestion, SPL queries, correlation rules, threat dashboards
  • Log analysis: Windows Event Logs, Linux auditd, firewall logs — structured threat hunting
  • Incident Response: NIST SP 800-61 — preparation, detection, containment, eradication, recovery
  • Digital forensics: chain of custody, FTK Imager disk imaging, Volatility memory forensics
  • AI-powered attacks: LLM prompt injection, AI-generated phishing at scale, adversarial ML basics
  • Capstone: full-scope penetration test report with executive summary and remediation roadmap
Capstone: Professional penetration testing engagement report
AI Threat Intel
Splunk AI
Kali Linux
Metasploit
Burp Suite
Wireshark
Nmap
Nessus
Volatility
Pacu
HackTheBox
TryHackMe
Industry Internship Phase · Months 4–6
Embedded · Live Work

Placement

Embedded in a security operations, GRC, or penetration testing team at a GCC, BFSI firm, or cybersecurity consultancy.

Deliverables

Vulnerability assessment reports, SOC alert triage, security audit documentation, incident response participation.

Credential

Professional Certificate in Cybersecurity, Scout Express placement profile, CEH-aligned competency endorsement, and internship letter.

Track 04 · BFSI & Capital Markets

AI in Banking,
Financial Services
& Insurance

BFSI is among the highest-velocity adopters of AI in India — from fraud detection and credit underwriting to robo-advisory and regulatory compliance automation. This track develops professionals who understand financial systems at the domain level and can apply AI tools to real banking and capital markets problems. NISM examination preparation is built into the curriculum for learners targeting front-office and distribution roles.

Programme Outcomes

  • Understand credit lifecycle, treasury operations, and capital markets structure end-to-end
  • Apply AI and data analytics to fraud detection, credit scoring, and risk modelling
  • Prepare for NISM Series V-A, VIII, and X-A examinations
  • Build and present financial models using Excel and Python
  • Navigate regulatory frameworks: RBI, SEBI, IRDAI, and DPDPA implications
  • Placement-ready for analyst, relationship management, and fintech AI roles in BFSI
Course · Months 1–3 · 12 Weeks
MODULE 01
Weeks 1–3
Banking Operations & Financial System Architecture
  • Indian banking structure: commercial banks, NBFCs, SFBs, payment banks, cooperative banks
  • Core banking systems: CBS architecture, T24/Finacle workflow, product and data flows
  • Liability & asset products: CASA, term deposits, retail loans, MSME working capital finance
  • Payment systems: UPI architecture, NEFT, RTGS, IMPS, NACH, SWIFT settlement mechanics
  • KYC/AML compliance: FATF standards, RBI KYC Master Direction, beneficial ownership rules
  • AI tools for banking: document review automation, LLM-powered customer query workflows
AI Workshop: AI-assisted KYC review and credit onboarding workflow design
MODULE 02
Weeks 4–6 · NISM Aligned
Capital Markets, Credit Risk & Regulatory Framework
  • Capital markets: primary (IPO/FPO/Rights) and secondary markets, exchange and clearing mechanics
  • Equity analysis: fundamental analysis, financial statement reading, DCF valuation, comparables
  • Fixed income: bond pricing, yield curves, duration, G-Secs, corporate bonds, credit spreads
  • Mutual funds: NAV mechanics, fund categories, SIP, performance attribution — NISM V-A prep
  • Credit underwriting: 5Cs framework, CIBIL interpretation, PD/LGD/EAD, Basel III/IV overview
  • Regulatory framework: RBI, SEBI, IRDAI, NPA classification, provisioning norms, IndAS 109
NISM V-A preparation integrated · Case study: full credit appraisal exercise
MODULE 03
Weeks 7–9
Financial Modelling & Data Analysis
  • Excel mastery: financial functions, dynamic arrays, pivot tables, scenario and sensitivity analysis
  • Integrated 3-statement model: P&L, Balance Sheet, Cash Flow built from scratch in Excel
  • Python for finance: pandas, yfinance, Matplotlib — financial charts and portfolio return analysis
  • Valuation models: DCF, LBO basics, comparable trading multiples, football field presentation
  • Risk modelling: VaR (historical, parametric, Monte Carlo simulation), stress testing frameworks
  • AI-assisted analysis: LLMs for earnings call synthesis, annual report summarisation, sector research
Project: 3-statement financial model for a listed Indian company
MODULE 04
Weeks 10–12
AI in BFSI, Fintech & Capstone
  • AI credit scoring: feature engineering on bureau + transactional data, ML model with SHAP explainability
  • Fraud detection: rule-based vs ML-based systems, anomaly detection, real-time scoring pipelines
  • India Stack: UPI 2.0, Account Aggregator framework, OCEN, DigiLocker — ecosystem and monetisation
  • BNPL, embedded finance, neo-banking: business models, regulatory challenges, risk considerations
  • Conversational AI in banking: LLM customer service, loan onboarding chatbot architecture design
  • Capstone: AI-driven BFSI solution in credit, fraud, or wealth management — panel review
Capstone: AI BFSI solution with BFSI practitioner panel
LLM Financial Analysis
AI Credit Scoring
Python (pandas)
Excel (Advanced)
Bloomberg basics
Scikit-learn
SHAP
Power BI
NISM Portal
Industry Internship Phase · Months 4–6
Embedded · Live Work

Placement

Embedded in a bank, NBFC, fintech, or financial services GCC — in analytics, relationship management, or product teams.

Deliverables

Credit analysis reports, data models, product research briefs, or client portfolio reviews — depending on team placement.

Credential

Professional Certificate in BFSI, NISM examination support, Scout Express placement profile, and internship letter.

Track 05 · Human Resources

AI in HR &
People
Analytics

The HR function is undergoing a structural transformation: from administrative overhead to a data-driven, AI-augmented strategic business function. This track develops HR professionals who can operate at the intersection of people science, business strategy, and AI tools — not HR professionals who know that AI exists. The curriculum is built for early-career HR practitioners who intend to build serious careers in HR Business Partnering, People Analytics, or Talent Strategy.

Programme Outcomes

  • Design and execute full-cycle talent acquisition using AI-assisted sourcing and assessment tools
  • Build people analytics dashboards and present data-driven HR insights to business stakeholders
  • Operate as a credible HR Business Partner — compensation, performance, and workforce planning
  • Apply Design Thinking to HR process design and employee experience improvements
  • Navigate employment law, compliance obligations, and ethical AI use in people management
  • Placement-ready for HRBP, talent acquisition, and people analytics roles in new-age companies and GCCs
Course · Months 1–3 · 12 Weeks
MODULE 01
Weeks 1–3
Strategic HR, AI Fluency & Organisation Design
  • HR function architecture: Dave Ulrich HRBP model, COE structure, shared services operating model
  • Organisation design: spans of control, job architecture, role clarity, grading frameworks
  • HR metrics that matter: eNPS, attrition rate, time-to-hire, offer acceptance, cost-per-hire
  • AI tools for HR: Claude/GPT for JD creation, interview design, policy drafting, communication at scale
  • Future of work: skills-based organisation, distributed workforce, GCC talent strategy frameworks
  • Design Thinking in HR: employee journey mapping, pain point identification, co-creation workshops
AI Workshop: 30-day onboarding experience designed end-to-end with AI tools
MODULE 02
Weeks 4–6
Talent Acquisition & Total Rewards
  • Recruitment architecture: demand planning, workforce requisition, sourcing strategy, channel ROI
  • AI sourcing: LinkedIn Recruiter AI, Eightfold, SeekOut — capability mapping and bias awareness
  • Structured interviewing: competency-based, behavioural, situational design and facilitation
  • Compensation philosophy: pay positioning, internal equity, external competitiveness, transparency
  • Salary benchmarking: Mercer/Aon/WTW methodology, percentile positioning, compensation band design
  • India compliance: CTC structuring, TDS, EPF/ESIC contributions, gratuity, labour code implications
Project: Recruitment process + compensation band design for a GCC analyst role
MODULE 03
Weeks 7–9
Performance Management & People Analytics
  • Performance management: OKR design and deployment, KRA/KPI frameworks, calibration processes
  • 360-degree feedback: design, facilitation, debrief, integration into development plans and PIPs
  • People analytics foundations: Excel HR dashboards, attrition analysis, headcount planning models
  • Python for HR analytics: pandas cohort analysis, survival analysis for attrition, flight risk scoring
  • Power BI for HR: HRIS data connections, workforce analytics dashboards, executive-ready reporting
  • Storytelling with data: presenting HR insights to CHROs, business heads, and leadership teams
Project: Attrition analytics dashboard with predictive flight risk model
MODULE 04
Weeks 10–12
HRBP Operations, Workforce Strategy & Capstone
  • HRBP operating model: stakeholder management, business partnering conversation frameworks
  • Workforce planning: supply-demand modelling, skill adjacency mapping, scenario planning
  • Employee relations: grievance management, disciplinary procedures, India labour law compliance
  • DEI strategy: inclusive hiring audits, pay equity analysis, representation tracking, ERG design
  • Ethical AI in HR: DPDPA 2023 implications, algorithmic bias in hiring, consent management
  • Capstone: strategic HR intervention — attrition challenge, pipeline gap, or culture transformation
Capstone: Strategic HR intervention brief with practitioner panel review
Claude for JDs
AI Sourcing Tools
AI L&D Personalisation
Excel (HR Analytics)
Power BI
Python (pandas)
Darwinbox
LinkedIn Recruiter
Workday basics
Industry Internship Phase · Months 4–6
Embedded · Live Work

Placement

Embedded in an HR function at a GCC, new-age enterprise, or fast-growing technology company — in HRBP, TA, or People Analytics teams.

Deliverables

Recruitment closures, people analytics reports, policy documentation, onboarding programme execution, or L&D project delivery.

Credential

Professional Certificate in AI in HR & People Analytics, Scout Express placement profile, practitioner endorsement, and internship letter.

Admission Process

Structured. Selective.

Cohort size is capped to maintain the quality of instruction and internship placements. Admissions are assessed, not first-come-first-served.

01
Application
Submit track preference, educational background, and a 150-word statement of intent at apply.genvarsity.com.
02
Admissions Review
An admissions counsellor reviews your application and schedules a 20-minute academic alignment call within 48 hours.
03
Fee & Seat Confirmation
Cohort seat confirmed upon fee payment. Programme fee: ₹60,000. EMI options available via partner institutions.
04
Onboarding
Pre-cohort orientation, platform access, and first-week preparation materials issued 7 days before cohort commencement.
Applications Open · Cohort 2026

Build the career
you were hired for.

Select your track. Commit to nine months of structured, practitioner-led transformation. Graduate placement-ready for the AI-first enterprise economy.

Apply Now — ₹60,000 Speak to Admissions