Enterprise AI startup RapidCanvas has raised $29 million in a Series B funding round as it looks to expand its AI engineering teams in India, the US, and Brazil.

The round was led by Vertex Ventures SEA & India. Hitachi Ventures joined as a new investor, while Accel, Peak XV Partners, Valley Capital Partners, and Titanium Ventures participated again reported Dealstreetasia.

The funding is a sign that investors are looking beyond AI chatbots and demos. They are backing companies that can help businesses turn AI ideas into working tools inside factories, finance teams, supply chains, and other daily operations.

What RapidCanvas does

RapidCanvas was founded in 2021 by Rahul Pangam and Uttam Phalnikar. Pangam is the company’s co-founder and CEO, while Phalnikar is co-founder and CTO.

The company works in an AI execution business. That means it does more than sell software and leave customers to figure it out. RapidCanvas combines its AI platform with engineers who work with customers to understand a business problem, build a custom solution, and help run it after launch.

For example, a manufacturer may have large amounts of data about inventory, machine output, supplier delays, and customer demand. RapidCanvas could help build an AI tool that identifies patterns, predicts delays, or helps production teams make faster decisions.

The company’s model is built around the idea that AI projects often fail when they remain stuck at the testing stage. Many businesses can experiment with AI, but putting it into real workflows safely and reliably is harder.

Why India is important to the expansion

As per Mint, RapidCanvas already has an office in Hyderabad and is now planning to grow its field AI engineering team across India, the US, and Brazil.

India offers a large base of software engineers, data professionals, and domain experts. For RapidCanvas, this could help it build and deploy AI applications for global customers while also serving Indian enterprises that are beginning to invest more actively in AI.

The company will also use the new capital to expand its go-to-market teams and its network of private-equity and advisory partners. In simple terms, it wants more people who can sell, support, and deliver its AI products to businesses.

A different approach from traditional IT services

RapidCanvas is entering a market traditionally served by large IT consulting firms and data-science teams. Instead of relying only on long consulting projects, it combines AI agents with engineers who remain involved in delivering results.

The company says its field AI engineers learn how a customer’s business works, create tailored applications, and remain accountable after the tool goes live.

This puts RapidCanvas in competition with enterprise AI platforms such as Databricks, Altair, and Quantexa, as well as large IT services companies building their own AI offerings. The competition will depend on which companies can deliver useful results without creating expensive, complicated systems.

What the $29M funding means

The Series B takes RapidCanvas’s total funding to $52.5 million, according to the company’s latest funding details.

The money gives the startup more room to hire engineers, improve its platform, and win enterprise customers. But the bigger challenge is proving that its AI tools can create measurable value. Businesses are becoming more careful about AI spending and want clear gains such as lower costs, faster operations, better forecasting, or improved customer service.

RapidCanvas has positioned itself around this gap between AI experimentation and real deployment. Its growth in India will be watched closely because the country is both a major engineering talent centre and an increasingly important market for enterprise AI.

AI jobs are no longer a future trend in India. They are already changing hiring plans, salary talks, and career choices for students, software developers, analysts, content teams, and business workers.

A recent LinkedIn-linked jobs report placed AI-led roles among India’s fastest-growing career options for 2026, with Prompt Engineer and AI Engineer drawing strong attention. At the same time, another concern is becoming hard to ignore. Many employees want to move into AI roles, but they do not yet have the right mix of technical skills, business thinking, and hands-on practice.

That gap is the real story. India has demand. India has talent. But the bridge between the two is still being built.

Why these AI roles are growing so fast and appearing so quickly

Companies are adding AI to customer support, software development, marketing, finance, hiring, data analysis, cybersecurity, and even legal work. This has created demand for people who can build AI systems and also people who can use AI tools well inside daily business work.

An AI Engineer usually works on models, data, automation, APIs, testing, and product integration. This person needs coding skills and a good understanding of machine learning.

A Prompt Engineer is different. This role is about giving clear instructions to AI tools so they produce useful, accurate, and safe results. A good prompt engineer understands language, context, user needs, and the limits of AI tools.

For example, a bank may need an AI assistant that explains loan options in simple language. The AI Engineer may build and connect the system. The Prompt Engineer may test how the assistant replies, improve the instructions, reduce wrong answers, and make sure the tone sounds helpful.

A fresh report says nearly 80% of India’s chief technology officials believe AI is creating job roles that did not exist a few years ago. This fits the rise of roles like Prompt Engineer, AI Engineer, AI Product Manager, AI Trainer, and AI Governance Specialist.

The important point is that AI is not only removing repetitive work. It is also changing how companies design teams. Earlier, a software team mainly needed developers, testers, product managers, and data analysts. Now, the same team may need people who can write better AI instructions, test AI output, check bias, protect data, and connect AI tools with business workflows.

For employees, this creates both opportunity and pressure. A person who understands their core job and learns AI can move faster than someone who only learns tools at a surface level. For example, a customer support executive who learns how to build AI-assisted reply workflows may become more valuable. A content professional who learns prompt testing, fact-checking, and AI editing can move into AI content operations. A developer who learns model APIs and AI safety checks can shift toward AI engineering.

But the gap is clear. Companies are creating new roles faster than many workers can prepare for them. This is why reskilling is no longer optional. Employees need hands-on practice, not just certificates.

Why the skills gap is becoming a problem

The demand for AI talent is rising faster than the supply of trained workers. A report citing Nasscom and McKinsey numbers said India had about 420,000 AI professionals in 2024, while industry demand was around 600,000. The same report said the shortfall could become much larger by 2026 if upskilling does not improve.

This does not mean every employee must become a machine learning expert. But it does mean workers need practical AI comfort.

Many employees can use ChatGPT or similar tools casually. But office use is different. A company expects accuracy, data safety, repeatable output, and clear business value. That is where many workers struggle.

The gap is not only about coding. It is also about knowing what question to ask, checking whether the answer is correct, understanding company data rules, and knowing when human judgment is needed.

What employees are struggling with

One big challenge is confusion. Many workers do not know whether they should learn Python, machine learning, prompt writing, data analytics, cloud tools, or AI product management first.

Another issue is shallow learning. Short online videos can help, but they rarely prepare someone for real work. A person may know how to write a prompt, but may not know how to test output quality or handle sensitive customer data.

There is also fear. Some employees worry that using AI will make their own role look replaceable. A Microsoft and LinkedIn workplace study found that many knowledge workers were already using generative AI, but some were hesitant to openly discuss it at work. That shows how fast adoption is moving, but also how uncomfortable the change feels for many people.

Freshers face a different problem. They may understand AI tools, but they often lack project experience. Employers want proof. A small portfolio, GitHub project, case study, or workflow demo can matter more than simply listing “AI skills” on a resume.

Prompt Engineer vs AI Engineer

These two roles are connected, but they are not the same.

An AI Engineer needs stronger technical skills. This includes programming, data handling, model evaluation, cloud deployment, and API integration. Someone moving into this role from software development, data science, or backend engineering may have a smoother path.

A Prompt Engineer needs clear writing, testing ability, domain knowledge, and a strong sense of user behavior. People from content, research, customer support, marketing, training, and operations can move toward this role if they learn AI tools properly.

The catch is that prompt engineering alone may not stay a narrow job forever. In many companies, it may become part of other roles. A marketer may need prompt skills. A developer may need prompt skills. A support manager may need prompt skills. So workers should treat prompting as a useful skill, not only as a job title.

Competitor roles growing around AI

Prompt Engineer and AI Engineer are not the only roles benefiting from the AI boom. Several nearby roles are also getting attention.

• Data Scientist remains important because companies need people who can understand patterns and make sense of large data sets.
• Machine Learning Engineer is close to AI Engineer but usually goes deeper into model building and training.
• AI Product Manager is growing because companies need people who can turn AI ideas into usable products.
• Automation Specialist is also in demand as businesses try to reduce repetitive work.
• Cybersecurity Analyst with AI skills may become more valuable as AI brings new security risks.

For many employees, the best move may not be to chase the most popular title. The smarter move is to combine AI with their existing field. A finance worker who learns AI reporting tools can become more useful. A content writer who learns AI-assisted research and fact-checking can improve output quality. A developer who learns AI APIs can build better products.

How employees can close the gap

1. The first step is to pick a direction. If you like coding, start with Python, APIs, data basics, and machine learning concepts. If you prefer business or communication, start with prompt writing, AI tool testing, workflow design, and domain knowledge.
2. Second, build small projects. Do not only collect certificates. Create something practical. For example, make a customer support prompt library, an Excel-to-insight AI workflow, a resume screening demo with ethical checks, or a chatbot for a small business use case.
3. Third, learn how to verify AI answers. This is a key skill. AI can sound confident even when it is wrong. Employees who can check facts, compare outputs, and improve accuracy will be more trusted at work.
4. Fourth, understand data privacy. Never paste confidential company data into public AI tools unless your company allows it. This simple habit can protect both the employee and the employer.

Conclusion with key takeaways

Prompt Engineer and AI Engineer roles are rising because companies want to use AI in real business work, not just experiments. But the biggest challenge is the gap between job demand and employee readiness.

India has a strong talent base, but workers need more hands-on learning, better guidance, and clearer company training.

Key takeaways

1. Prompt Engineer and AI Engineer jobs in India are among the fastest-growing AI-led career options.
2. AI Engineer roles need coding, data, model, and product integration skills.
3. Prompt Engineer roles need clear communication, testing, domain knowledge, and AI tool understanding.
4. The real gap is practical skill, not interest.
5. Employees should build projects, learn verification, and combine AI with their current field.

Tata Consultancy Services, India’s largest IT services company, has made a bold prediction about the future of work. TCS Chairman N Chandrasekaran has said the company could have as many AI agents as human employees within the next three years. That line sounds dramatic at first. It may make many people wonder whether AI will replace lakhs of IT workers. But the real meaning is more layered. TCS is not simply talking about removing humans and adding software. It is talking about a new operating model where human employees and AI agents work together on coding, testing, support, operations, documentation, customer service and business processes.

This is a major signal for India’s IT industry. If TCS moves in this direction, other companies may follow faster. The next few years could change hiring, training, productivity and the type of skills young professionals need.

What TCS actually said

At TCS’ 31st Annual General Meeting, Chairman N Chandrasekaran said the company may have an equal number of AI agents and human employees in about three years. He also said the company will not hire at the same large scale as it did in the past, because AI agents will take over parts of work. TCS is already working on what it calls a human plus AI model. The company has created internal AI transformation programs, built AI tools for services, and set up a dedicated AI and service transformation unit.

In simple words, TCS wants AI to become part of normal delivery work, not just a side experiment.

About TCS and why this statement matters

Tata Consultancy Services was founded in 1968 and is part of the Tata Group. It is one of the world’s largest IT services and consulting companies, serving clients across banking, retail, telecom, manufacturing, healthcare, insurance and many other sectors. Because TCS employs lakhs of people and works with major global companies, its AI plans are closely watched. When a smaller startup says it will use AI agents, it is interesting. When TCS says it, the impact can reach employees, clients, colleges, freshers and the broader job market.

This is why the statement has become a major workforce story.

What are AI agents

An AI agent is a software system that can complete tasks with some level of independence. A normal chatbot may answer a question. An AI agent can understand a goal, take steps, use tools and report back with results. For example, in an IT project, an AI agent may check code, find bugs, write test cases, prepare documentation or monitor system alerts. In HR, it may help with onboarding, employee queries or training recommendations. In finance, it may process invoices, flag errors or prepare reports.

The important point is that AI agents still need human supervision, especially in complex work. They can move faster than humans in repetitive tasks, but they may make mistakes, misunderstand context or need approval for sensitive decisions.

How this could change TCS jobs

The first big change may come in routine work. Tasks such as basic code writing, testing, data checking, ticket handling, report generation and documentation could become more automated.

This does not mean every job disappears. It means the shape of many jobs may change. A software engineer may spend less time writing basic code and more time reviewing AI-generated code, designing architecture and solving difficult problems. A support engineer may use AI to diagnose issues faster. A business analyst may use AI to prepare first drafts of process maps or requirement documents.

The employee’s value may shift from doing every small step manually to guiding, checking and improving AI-supported work.

Impact on freshers and hiring

Freshers may feel the biggest pressure. Indian IT companies have traditionally hired large numbers of graduates and trained them on projects. If AI agents handle entry-level repetitive work, companies may reduce volume hiring or expect freshers to become productive faster. This is why skill-building becomes very important. Basic coding alone may not be enough. Freshers will need to understand AI tools, cloud platforms, data, cybersecurity basics, business processes and prompt-based workflows.

A fresher who can use AI responsibly, verify outputs and understand business context may have a better chance than someone who only knows textbook programming.

Will AI agents replace employees

Some jobs will definitely be affected. Companies may need fewer people for low-complexity, repetitive tasks. Hiring may become more selective. The old model of adding more people for every new project may slowly weaken. But replacing humans fully is not simple. Enterprise IT work involves judgment, client communication, accountability, security, domain knowledge and problem-solving under pressure. AI agents can support this work, but they cannot take full responsibility in the way experienced professionals do.

So the better way to understand this shift is not “AI versus humans.” It is “humans who know how to work with AI versus humans who do not.”

What employees should learn now

Employees should start building practical AI skills. This does not mean everyone must become an AI scientist. But everyone should understand how AI tools can improve their own work. Software professionals can learn AI-assisted coding, testing automation, cloud AI platforms and model evaluation. Business analysts can learn data interpretation, workflow automation and AI documentation tools. Support teams can learn AI monitoring, ticket triage and incident response systems.

Soft skills will also matter more. Communication, judgment, client handling, ethics and domain understanding will become harder to automate.

What it means for clients

For TCS clients, AI agents could mean faster delivery and lower operating costs. Projects may move quicker if AI can prepare drafts, run tests, scan systems and reduce manual effort. A bank may get faster software updates. A retailer may improve customer support. A telecom company may detect network issues earlier. A manufacturer may use AI to predict equipment problems.

But clients will also ask tough questions. Is the AI secure? Who is responsible if it makes a mistake? Can the system explain its decisions? Does it follow data protection rules? These questions will shape the future of enterprise AI.

Competitors and industry check

TCS is not alone in this race. Infosys, Wipro, HCLTech, Tech Mahindra, Cognizant, Accenture, IBM and Capgemini are also investing heavily in AI-led services. Indian IT companies are also using tools from Microsoft, Google Cloud, AWS, ServiceNow, GitLab and other enterprise platforms. Many are building internal AI labs, industry-specific AI solutions and employee training programs.

This means the competition will not be only about headcount. It will be about who can deliver better AI-supported outcomes for clients.

Conclusion with key takeaways

TCS’ plan to have as many AI agents as human employees in three years is one of the strongest signs yet that India’s IT services model is changing. The future may not be about replacing every worker, but it will certainly reward people who can work well with AI.

Key takeaways

• TCS Chairman N Chandrasekaran said the company may have as many AI agents as human employees in three years.
• AI agents are software systems that can perform tasks such as coding support, testing, documentation, monitoring and process automation.
• TCS may not hire at the same scale as before because AI can handle parts of routine work.
• Freshers and employees will need stronger AI, cloud, data and business skills.

The future workforce will likely be human plus AI, where people guide, verify and improve AI-driven work.

Facts Input- BS