Six hundred plus institutes in India advertise AI courses in India in 2026, with fees spanning ₹4,000 to ₹4.5 lakh and almost no clarity on what is actually taught or what placement outcomes look like. This is the no-marketing buyer’s guide. It explains what an AI course must include to make a candidate hireable, the realistic salary bands for engineers fresh out of training, and an honest comparison between cert-only courses and structured placement-track programmes that include a 4-month paid internship. Reviewed by Mr. Vikas Swami, Dual CCIE #22239 and founder of five production AI and SaaS products serving 20 million Indian businesses.
In 2026, the term 'AI course in India' has become a marketing umbrella that covers everything from a ₹4,000 recorded video on prompt engineering to a ₹4.5 lakh university-stamped postgraduate diploma. Over 600 institutes now advertise some variant of an artificial intelligence course in India, yet the syllabus, duration, and outcome transparency remain minimal. Candidates are left to navigate a fee spectrum that spans two orders of magnitude without a clear way to distinguish between a certificate-mill course and a programme that actually produces hireable AI engineers.
A hireable AI engineer in 2026 needs two things: classical machine learning and deep learning foundations, and modern generative AI tooling. Most institutes teach only one side of this equation. The ₹4,000-₹15,000 video courses focus exclusively on prompt engineering and basic LangChain workflows, while the ₹2 lakh-plus university diplomas often over-index on theoretical ML without touching production retrieval-augmented generation systems. The result is a market where candidates exit training with either a toy project or a paper credential, neither of which is sufficient for technical screening rounds at product companies or global capability centres.
The placement claims are equally opaque. Many institutes advertise 'AI course in India with placement' but the fine print reveals aspirational language rather than contractual guarantees. A 12-week bootcamp may claim to place candidates, but the reality is that most graduates are not ready for the evaluation-harness and observability questions that hiring managers ask. The safer path is a structured 8-month programme that includes both training and a paid internship phase, allowing the candidate to touch real systems before entering placement rounds.
An AI course in India that aims to produce hireable engineers in 2026 must cover a specific syllabus checklist. The non-negotiables include Python for AI, machine learning fundamentals, deep learning with TensorFlow or PyTorch, transformer architecture, LangChain and LangGraph, retrieval-augmented generation with vector databases such as Pinecone or Weaviate, prompt engineering, AI agents, MLOps basics, evaluation harnesses, and deployment with Hugging Face or cloud inference endpoints. Each of these modules serves a distinct purpose in production AI systems.
Python for AI is the foundational language. Without strong Python skills, candidates cannot debug retrieval pipelines or write custom evaluation metrics. Machine learning fundamentals cover supervised and unsupervised learning, evaluation metrics, and bias-variance trade-offs. Deep learning with TensorFlow or PyTorch is essential for understanding transformer architecture, which underpins all modern generative AI models. LangChain and LangGraph are the standard frameworks for building AI agents and multi-step workflows, while RAG with vector databases is the default pattern for enterprise retrieval systems.
Prompt engineering is often dismissed as trivial, but production prompt engineering involves failure-mode testing, retrieval-quality tuning, and observability integration. AI agents require planning, tool use, and reliability engineering, which most short courses skip entirely. MLOps basics cover model versioning, deployment pipelines, and observability, while evaluation harnesses are critical for measuring retrieval quality and agent reliability. Deployment with Hugging Face or cloud inference endpoints is the final step that turns a project into a production-ready system. The table below outlines the syllabus checklist that separates a hireable AI engineer from a candidate with a paper credential.
| Module | Must include | Why it matters |
|---|---|---|
| Python for AI | async, typing, numpy, pandas, basic data engineering | Foundation of every AI codebase in production |
| Machine learning fundamentals | supervised, unsupervised, evaluation metrics, cross-validation | Hiring managers still screen on classical ML basics |
| Deep learning with TensorFlow or PyTorch | neural networks, backpropagation, transformer architecture, attention | Required to reason about model behaviour and fine-tuning |
| Generative AI tooling | LangChain, LangGraph, prompt engineering, Hugging Face models | Core stack for any GenAI application in 2026 |
| RAG with vector databases | Pinecone, Weaviate, pgvector, retrieval failure-mode debugging | The most commonly screened production AI skill |
| AI agents and agentic workflows | tool use, multi-step planning, agent reliability patterns | Where the highest-paying 2026 AI roles are emerging |
| MLOps and AI deployment | containerisation, inference endpoints, model versioning | What separates a course-completer from a production engineer |
| Evaluation and observability | LangSmith-style harnesses, eval datasets, drift detection | The skill most courses skip entirely |
The fee for an AI course in India in 2026 ranges from ₹4,000 to ₹4.5 lakh, and the value delivered at each tier varies dramatically. The ₹4,000-₹15,000 tier consists of recorded video courses that provide a certificate but no hands-on projects, mentorship, or placement support. These are suitable only for working professionals who already have a technical background and need a quick introduction to generative AI concepts. The ₹20,000-₹60,000 tier includes live cohort courses with limited mentor time, but the projects are often toy examples that do not translate to production readiness.
The ₹70,000-₹1.5 lakh tier is where classroom or hybrid programmes with capstone projects begin. These programmes typically include 3-4 months of training and a project phase, but placement support is often aspirational rather than contractual. The ₹2 lakh-₹4.5 lakh tier consists of university-stamped postgraduate diplomas that include stipend support during the programme, but the syllabus is often theoretical and lacks modern GenAI tooling. The ₹95,000-₹1.2 lakh tier is where structured placement programmes with paid internships and contractual placement guarantees emerge. These programmes are the safest outcome bet for freshers and career switchers because they include both training and a 4-month internship phase, allowing candidates to build real-world experience before entering placement rounds.
The table below breaks down the fee tiers and what each one actually delivers in terms of training, projects, mentorship, and placement support.
| Tier | Fee Range (INR) | Format | What is included |
|---|---|---|---|
| Recorded video only | ₹4,000 – ₹15,000 | Self-paced video + PDF notes | Cert of completion only; no live trainer, no placement, no project review |
| Live online cohort | ₹20,000 – ₹60,000 | Zoom live + recorded + community | Live mentor sessions, capstone project, no placement guarantee |
| Classroom or hybrid programme | ₹70,000 – ₹1,50,000 | Classroom + on-site lab + projects | In-person trainer, lab access, capstone, soft placement assistance |
| University-stamped PG diploma | ₹2,00,000 – ₹4,50,000 | Hybrid + university brand | Cert with university name, mixed placement data, longer duration |
| 8-month placement-track programme | ₹95,000 – ₹1,20,000 | Classroom + 4-month paid internship | AI-in-domain module, paid internship, contractual placement guarantee, Verified Experience Letter |
The AI course landscape in India varies by city due to differences in hiring demand, industry concentration, and cost of living. Bangalore remains the epicentre for AI hiring, with global capability centres and product companies driving demand for AI engineers. Hyderabad has emerged as a strong second, with Microsoft and Amazon AI research and development centres creating a pipeline for AI talent. Pune is the auto-AI and enterprise AI hub, with a focus on AI applications in manufacturing and large-scale enterprise systems. Delhi NCR is the services-firm AI market, with Tier-1 IT services firms building AI services divisions for global clients.
Chennai is the BFSI-AI and product-engineering AI market, with banks and financial services firms building internal LLM platforms and product companies hiring for AI-driven engineering roles. Mumbai is the BFSI and fintech AI market, with a strong demand for AI engineers in risk modelling, fraud detection, and customer analytics. While online courses have flattened fee differences across cities, classroom programmes in tech parks such as HSR Layout, Outer Ring Road, Whitefield, and Manyata still command a premium due to the networking and mentorship benefits they provide.
The table below compares the AI course landscape and hiring market across major Indian cities in 2026.
| City | AI course fee range | Dominant AI hiring segment |
|---|---|---|
| Bangalore | ₹15,000 – ₹4,50,000 | Product companies, vendor R&D, GCCs, AI-native start-ups |
| Hyderabad | ₹15,000 – ₹3,50,000 | Big-tech R&D, GCCs, BFSI AI platforms |
| Pune | ₹15,000 – ₹2,80,000 | Auto-AI, enterprise services, GCC expansion |
| Delhi NCR (Gurugram, Noida) | ₹15,000 – ₹3,20,000 | Services-firm AI divisions, fintech, edtech |
| Chennai | ₹14,000 – ₹2,50,000 | BFSI AI, product engineering, services delivery |
| Mumbai | ₹15,000 – ₹3,00,000 | BFSI AI, fintech, media-AI, services delivery |
Online AI courses in India are a viable option for working professionals with strong self-discipline and an existing technical background. They offer flexibility and cost savings, but they are not the best choice for freshers or career switchers. AI debugging is heavily lab-driven, and remote-only candidates often plateau after the first 8 weeks because they lack the immediate feedback and peer collaboration that classroom environments provide. Classroom or hybrid programmes, on the other hand, offer structured schedules, hands-on lab access, and real-time mentor support, which are critical for mastering production AI systems.
Hybrid programmes, which combine classroom training with 24x7 lab access, are emerging as the default choice for quality institutes. They provide the best of both worlds: the structure and mentorship of a classroom environment, and the flexibility of online access for debugging and project work. Pure recorded-video AI courses produce the worst outcome data because they lack the hands-on component that hiring managers screen for. Candidates who complete only recorded courses often struggle in technical interviews because they have never debugged a retrieval pipeline or fine-tuned an evaluation harness.
The choice between online and classroom should be based on the candidate’s profile. Working professionals with 2+ years of IT experience can succeed with online courses if they have the discipline to complete projects independently. Freshers and career switchers, however, are better served by classroom or hybrid programmes that provide the structure and support needed to build production-ready AI skills.
The 12-week generative AI bootcamp has become a popular format in India, but it is not sufficient for producing hireable AI engineers. A 12-week sprint can teach prompt engineering, basic LangChain workflows, and a toy retrieval-augmented generation project, but it cannot cover the evaluation harnesses, production observability, fine-tuning fundamentals, vector-index sizing, retrieval failure modes, agent reliability, or any of the other topics that hiring managers probe in technical rounds. Many bootcamp graduates are surprised by interview rejections because they lack the depth required for production AI roles.
The core issue is that 12 weeks is not enough time to build both the foundational knowledge and the hands-on experience needed for AI engineering. Machine learning fundamentals, deep learning with TensorFlow or PyTorch, and transformer architecture alone require 6-8 weeks of focused study. Adding modern GenAI tooling such as LangChain, LangGraph, and RAG with vector databases leaves no time for production topics like deployment, observability, or evaluation harnesses. Bootcamps often skip these critical modules entirely, leaving graduates unprepared for the technical screens that hiring managers conduct.
The alternative is a longer programme that includes an internship phase. A 4-month training programme followed by a 4-month paid internship allows candidates to touch real systems, debug retrieval pipelines, and build production-ready AI applications. This structure produces engineers who are ready for placement rounds, rather than candidates who hold a paper credential but lack the skills to pass technical interviews.
The salary after an AI course in India in 2026 depends on the candidate’s profile, prior experience, and the type of training completed. Fresher AI engineers with only a course certificate and no production exposure typically earn ₹4-7 LPA. Those with a course certificate and a capstone project on GitHub can expect ₹5-9 LPA, while freshers who complete a course and a 4-month paid internship earn ₹7-12 LPA. The internship phase is critical because it provides the real-world experience that hiring managers screen for in technical interviews.
Working software developers with 2-4 years of experience who add AI skills to their profile can expect ₹14-22 LPA, while working data engineers transitioning to AI roles earn ₹16-26 LPA. Senior AI engineers with 5-8 years of experience command ₹25-40 LPA, with exceptional product-company roles capping at ₹50 LPA. It is important to note that the ₹40 LPA-for-fresher claims circulating on social media are not the typical case. These numbers are outliers and should not be used as a benchmark for expected outcomes.
The table below outlines the realistic salary bands for AI engineers in India in 2026, segmented by profile and experience level.
| Candidate profile | Salary band (INR LPA) | Typical role |
|---|---|---|
| Fresher, cert only, no production exposure | ₹4 – ₹7 LPA | Junior AI Engineer, ML Associate |
| Fresher, cert + capstone project on GitHub | ₹5 – ₹9 LPA | Junior AI Engineer with portfolio |
| Fresher, programme + 4-month paid internship | ₹7 – ₹12 LPA | AI Engineer L1 with verified experience |
| Working software developer (2-4 yrs) adding AI | ₹14 – ₹22 LPA | AI Engineer, GenAI Application Engineer |
| Data engineer transitioning to AI | ₹16 – ₹26 LPA | AI Platform Engineer, ML Engineer |
| Senior AI engineer (5-8 yrs) | ₹25 – ₹40 LPA | Senior AI Engineer, Lead ML Engineer |
| Exceptional product-company AI roles | Up to ₹50 LPA | Staff AI Engineer (rare, top product firms) |
The hiring market for AI engineers in India in 2026 is segmented across several employer types. Product companies, vendor research and development centres, and global capability centres are the primary drivers of AI hiring, with a focus on building internal LLM platforms and AI-driven products. Tier-1 IT services firms have also created AI services divisions to serve global clients, while BFSI organisations are building internal AI platforms for risk modelling, fraud detection, and customer analytics. Healthtech and edtech product firms are hiring AI engineers to build AI-driven applications, and AI-native start-ups are emerging as a niche but competitive market for AI talent.
The hiring funnel for AI engineers is heavily portfolio-driven. A GitHub repository with production-ready projects matters more than a certificate, and technical screens are rigorous. Hiring managers probe for hands-on experience with retrieval-augmented generation, AI agents, evaluation harnesses, and deployment pipelines. System design rounds increasingly include questions about retrieval pipeline architecture, vector database sizing, and observability integration. Candidates who lack these skills are often filtered out early in the interview process.
The table below outlines the employer segments that hire AI engineers in India and the types of roles they offer.
Free and freemium AI learning resources can take a candidate to 60-70 percent technical readiness, but they cannot replace the placement infrastructure of a structured programme. The Hugging Face course is a free, hands-on resource that covers transformer architecture, fine-tuning, and deployment. DeepLearning.AI offers courses on Coursera that can be audited for free, covering machine learning fundamentals and deep learning with TensorFlow. Leading researcher YouTube channels provide free tutorials on building neural networks from scratch, while the official LangChain and LangGraph documentation is an excellent resource for learning AI agent workflows.
The Anthropic and OpenAI cookbook repositories are free and contain practical examples of retrieval-augmented generation, prompt engineering, and evaluation harnesses. Kaggle notebooks are another free resource for hands-on machine learning practice, with datasets and competitions that allow candidates to build and evaluate models. While these resources are valuable, they lack the mentorship, project guidance, and placement support that a structured programme provides. Candidates who rely solely on free resources often struggle to build a portfolio that meets hiring manager expectations.
The table below lists the free and freemium AI learning resources that are worth using, along with their key features and limitations.
Hiring managers in India screen AI engineers for a specific skill stack that spans six layers. The first layer is language fundamentals, which includes Python, asynchronous programming, and type hints. Without strong Python skills, candidates cannot debug retrieval pipelines or write custom evaluation metrics. The second layer is machine learning fundamentals, covering supervised and unsupervised learning, evaluation metrics, and bias-variance trade-offs. The third layer is deep learning fundamentals, which includes transformer architecture, attention mechanisms, and fine-tuning techniques.
The fourth layer is generative AI tooling, which covers LangChain, LangGraph, retrieval-augmented generation, and vector databases such as Pinecone or Weaviate. The fifth layer is production AI, which includes deployment, observability with tools like LangSmith, and evaluation harnesses. The sixth and final layer is AI agents, which covers planning, tool use, and multi-step workflows. Most AI courses in India miss the production and agent layers entirely, leaving graduates unprepared for technical interviews.
The table below outlines the AI engineer skill stack that hiring managers screen for in India, along with the key topics covered in each layer.
| Skill layer | What it covers | Most courses skip |
|---|---|---|
| Language fundamentals | Python async, typing, packaging, FastAPI | Production packaging and async handling |
| ML fundamentals | Supervised, unsupervised, evaluation metrics | Honest evaluation discipline |
| DL fundamentals | Transformer architecture, attention, fine-tuning | Fine-tuning beyond toy examples |
| GenAI tooling | LangChain, LangGraph, prompt engineering | LangGraph and structured-output discipline |
| Production AI | Deployment, observability, evaluation harnesses | Evaluation harness and drift detection |
| AI agents | Tool use, planning, multi-step workflows | Agent reliability and failure-mode patterns |
Evaluating an AI course in India before enrolling requires a systematic approach. The 12-point checklist below covers the key questions that candidates should ask. First, does the syllabus include both classical machine learning and modern generative AI tooling? Second, is there hands-on retrieval-augmented generation with at least one production vector database? Third, is there an evaluation-harness module that covers retrieval quality and agent reliability? Fourth, what is the lab access policy, and is it 24x7 or restricted to classroom hours?
Fifth, is there a paid internship phase that allows candidates to touch real systems? Sixth, is the placement claim contractual or aspirational? Seventh, who is the lead trainer, and what is their production-AI track record? Eighth, what is the actual placement data for the last cohort, and is it verifiable? Ninth, are fees inclusive of GST and exam vouchers, or are there hidden costs? Tenth, is there EMI support for candidates who cannot pay the full fee upfront?
Eleventh, is the certificate verifiable by employers, and is there a public portal for validation? Twelfth, is there post-programme alumni support, such as access to updated course materials or mentorship? Institutes that refuse to answer 8 or more of these questions honestly are not worth the fee. The goal is to find a programme that produces an employable AI-capable engineer, not just a certificate holder.
Many freshers enter the AI job market with the goal of landing a 'pure AI' role, but the reality is that the largest hiring volume in India in 2026 is for engineers who can apply AI inside an existing domain. Networking automation, network security, security operations centre operations, cloud security, and DevOps are all domains where AI is augmenting traditional engineering roles. A pure AI engineer role is rarer and more competitive than the marketing suggests, while an AI-augmented engineering role is both more accessible and more stable for early-career candidates.
An 8-month placement programme that pairs a domain with an AI-in-domain module produces a candidate who can land an AI-augmented engineering role faster than a pure AI cert holder. For example, a network engineer who learns AI-driven network automation is more hireable than a pure AI engineer with no domain expertise. The same logic applies to security engineers, cloud engineers, and DevOps engineers. The AI module inside these programmes is not theoretical; it is grounded in the specific use cases of the domain, making the candidate immediately valuable to employers.
This is the model that Networkers Home follows. The three placement programmes — Full Stack Network Engineering, Full Stack Network Security, and Cloud Security and Cybersecurity — each include an AI-in-domain module as the final phase. The founder’s production AI portfolio ensures that the AI training is non-theoretical and aligned with real-world applications. For freshers and career switchers, this is often the safer outcome bet than a standalone AI course.
Networkers Home offers three 8-month placement-track programmes that embed AI as a final module, designed for candidates who want to enter the AI job market in 2026. The Full Stack Network Engineering programme covers CCNA, CCNP Enterprise, SD-WAN, and network automation with Python and Ansible, culminating in an AI in network operations module. The Full Stack Network Security programme includes CCNP Security, multi-vendor firewall tracks, SD-WAN security, and an AI in network security module. The Cloud Security and Cybersecurity programme covers Linux, penetration testing, AWS, cloud security, DevSecOps, container security, and SOC operations with AI-assisted detection-engineering.
Each programme is priced at ₹96,000 inclusive of GST and includes 4 months of training followed by a 4-month paid internship. The internship phase allows candidates to apply their AI skills in a real-world environment, building the experience that hiring managers screen for. The programmes also include a contractual placement guarantee, ensuring that candidates who meet the programme requirements are placed in relevant roles. Additionally, candidates receive 12 months of free access to NHPREP.COM, a mock test platform for certification preparation.
The AI modules in these programmes are grounded in the founder’s production AI portfolio. CrawlCrawl, 24Observe, AeoNiti, Quick21, and 21Bill are AI and SaaS products that serve over 20 million Indian businesses. 21Bill, in particular, is a GSTN-approved invoicing platform that processes over ₹500 crore in transactions, demonstrating the real-world application of the AI skills taught in the programmes. This context ensures that the AI training is non-theoretical and aligned with industry needs.
The choice between a pure AI course and an AI-augmented placement programme depends on the candidate’s profile and career goals. For freshers with no IT background, an AI-augmented placement programme is almost always the safer outcome bet. These programmes provide the domain expertise and structured placement support needed to land an entry-level role, while a pure AI course leaves the candidate without a clear path to employment. The 8-month duration and paid internship phase ensure that the candidate exits with both technical skills and real-world experience.
Working software developers with 2+ years of IT experience can opt for a pure AI upskilling course if their goal is to add AI skills to an existing role. These candidates already have the domain expertise and professional network needed to transition into AI-augmented roles, and a shorter course is sufficient for upskilling. Career switchers from non-IT backgrounds, such as mechanical engineering, civil engineering, or commerce, are better served by an 8-month placement programme with an embedded AI module. The domain training provides the foundational knowledge needed to enter the IT job market, while the AI module ensures that the candidate is competitive in 2026.
The question is not 'which AI course is best in India' but 'which path produces an employable AI-capable engineer at the end of 8 months'. For most freshers and career switchers, the answer is an AI-augmented placement programme that includes both training and a paid internship phase.
Candidates often overlook critical questions about AI courses in India until after they have enrolled, leading to frustration and suboptimal outcomes. One common question is how much lab time is real versus reading. Many institutes advertise 'hands-on' courses but deliver only theoretical content, leaving candidates unprepared for technical interviews. Another overlooked question is whether the AI deployment module uses real cloud infrastructure or a sandbox. Production AI systems require cloud deployment, and candidates who train only on sandboxes struggle to debug real-world systems.
Candidates also ask what happens if a placement round fails. Some institutes offer re-attempts, while others provide no support after the training phase. The policy for switching programmes mid-way is another critical question, as candidates may realise that their chosen specialisation is not the right fit. Additionally, candidates often assume that GST is included in the fee, only to discover hidden costs later. EMI options are another area where transparency is lacking, with some institutes offering flexible payment plans and others requiring full upfront payment.
Finally, candidates ask whether the certificate is verifiable by employers. Many institutes provide paper certificates that cannot be validated, leaving candidates unable to prove their credentials. These questions highlight the importance of thorough evaluation before enrolling in an AI course in India. The FAQ section below addresses these and other commonly overlooked questions.
I cleared my first CCIE in October 2008 and the second in January 2009, both within 90 days. The cert gave me a career, but it also taught me that a credential alone is not enough. You need real-world experience to be hireable, and that is why I founded Networkers Home in 2007. Over the last 19 years, we have placed over 45,000 engineers across 800+ hiring partners, and the principle remains the same: training must lead to employability, not just a certificate.
Today, I also run five AI and SaaS products — CrawlCrawl, 24Observe, AeoNiti, Quick21, and 21Bill. 21Bill alone is trusted by over 20 million Indian businesses and has processed over ₹500 crore in invoices. This context is why our AI modules are non-theoretical. We teach what we use in production, not what is trending on social media.
The market is flooded with 12-week generative AI bootcamps that promise ₹40 LPA salaries for freshers. The reality is that most of these candidates are not ready for technical interviews. A hireable AI engineer in 2026 needs both classical ML foundations and modern GenAI tooling, and that takes time. An 8-month programme with a paid internship phase is the safer outcome bet for freshers and career switchers.
My advice is simple: make the rational choice for your specific stage. If you are a fresher or a career switcher, an AI-augmented placement programme is the safer path. If you are a working professional with 2+ years of IT experience, a pure AI upskilling course may be sufficient. Do not chase the hype. Focus on what produces an employable engineer at the end of 8 months.
If you want to discuss which path is right for you, WhatsApp me at +91 96110 27980 or email vikas@networkershome.com.
Verified placements with company name, role, and CTC. All graduates were trained at HSR Layout campus and placed via the 800+ hiring partner network.
“The founder Vikas Swami sir has actually built QuickZTNA and QuickSDWAN using AI-first development — the curriculum reflects real production AI engineering, not academic theory. I shipped 50+ AI projects across LangChain RAG, agent frameworks, vector DBs. Hired as an AI Engineer at ₹16 LPA at a Bangalore product company.”
“LangChain, LangGraph, Pinecone, Weaviate, OpenAI and Claude APIs — modern stack covered with depth. Production-ready focus with prompt engineering, evaluation harnesses, observability via LangSmith. The 4-month internship let me build real GenAI applications. Now a GenAI Developer at ₹13 LPA.”
“MLOps integration with FastAPI, Docker, model deployment, cost-optimisation across model tiers — the production-engineering side of AI most courses skip. Plus the AI-first curriculum across other tracks (networking, security, cloud) showed me how AI is being integrated into every IT discipline. Joined IBM India as AI/ML Engineer.”
Networkers Home runs three 8-month placement-track programmes, each structured as four months of intensive classroom and lab training followed by four months of paid internship inside the institute's own operations division. Every programme includes an AI-in-domain module in the final phase. Total fee is ₹96,000 inclusive of GST, with EMI options available, and the programmes carry a contractual placement guarantee detailed on the refund policy page.
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