AI careers in India could be entering a new phase as companies look beyond building artificial intelligence models and focus on putting them to work. A new industry report highlights the growing demand for forward-deployed engineers, professionals who combine software engineering, AI expertise and customer-facing problem-solving.
According to the India’s FDE Opportunity Report by Scaler and CyberMedia Research (CMR), 88% of surveyed Indian enterprises expect demand for forward-deployed engineering capabilities to increase by 2030. Yet, only 1% of the surveyed engineers currently work in explicitly designated forward-deployed engineering roles.
The findings point to a potential opportunity for India’s technology workforce. However, the role also requires a broader skill set than traditional coding alone.
What is a forward-deployed engineer?
A forward-deployed engineer, commonly known as an FDE, helps companies turn technology into working solutions for real business problems.
Unlike developers who primarily build software, FDEs work closely with customers to understand their needs. They then adapt, integrate and deploy software or AI systems within the customer’s existing environment.
For example, a company might want to use AI to automate customer support. Building a chatbot is only the first step. The company must also connect it to its existing databases, ensure it provides useful responses, protect sensitive information and monitor its performance.
An FDE can help manage this process from initial requirements to deployment.
The role therefore combines technical development with practical implementation. It also requires engineers to communicate with customers, understand business priorities and troubleshoot problems after a product goes live.
Why AI careers in India could change
For years, many software engineering roles have focused heavily on writing code, developing applications and maintaining systems.
However, businesses increasingly want measurable results from their AI investments. A demonstration may show that an AI tool works, but deploying it reliably across an organisation presents a different challenge.
Companies must connect AI systems to existing software, manage data, address security requirements and ensure that the technology fits their workflows.
This creates demand for professionals who can bridge the gap between AI development and business implementation.
The Scaler–CMR report found that 98% of surveyed enterprises believe forward-deployed engineering will move beyond being a niche role. It also found that 58% of surveyed engineers were interested in transitioning into these roles.
These findings suggest growing interest in the specialisation. Still, actual employment opportunities will depend on companies converting their expectations into hiring plans.
What skills do companies want?
The emerging role requires a combination of engineering knowledge, deployment experience and communication skills.
1. Applied artificial intelligence
Engineers need to understand how AI tools work and how to apply them to specific business problems. Depending on the job, this may involve large language models, AI APIs, retrieval-augmented generation or AI agents.
The goal is not simply to build an impressive prototype. Engineers must understand whether the solution works reliably for its intended users.
2. Software development and system integration
Strong programming fundamentals remain important. Engineers may need to build applications, connect APIs, work with databases and integrate AI systems with existing enterprise software.
Knowledge of software architecture, authentication and data handling can also help when working with production systems.
3. Production deployment and troubleshooting
A working prototype is not the same as a dependable production system.
Companies need engineers who can deploy applications, investigate failures, monitor performance and resolve technical problems.
The Scaler–CMR report found that 77% of surveyed enterprises prioritised production deployment skills, while only 19% reported adequate talent availability in this area.
This gap highlights why practical experience could be particularly valuable for aspiring FDEs.
4. Customer communication
Forward-deployed engineers often work directly with customers or business teams. They must understand what users need, explain technical trade-offs and translate business requirements into workable solutions.
The report found that 61% of surveyed enterprises prioritised customer engagement for these roles. Meanwhile, 37% of surveyed engineers identified it as a major career barrier.
Technical ability matters, but the capacity to solve the right problem is equally important.
Could this career offer higher salaries?
Compensation is one reason the role is attracting attention.
The Scaler–CMR report found that 80% of engineers working in explicitly designated FDE roles reported annual incomes between ₹40 lakh and ₹1 crore. The report also suggested that these roles could command a salary premium of up to 30%.
These figures should be interpreted carefully. They describe the surveyed professionals in a specialised role, not the salary that every engineer entering the field can expect.
Actual compensation can vary significantly according to experience, employer, location, technical responsibilities and the complexity of the projects involved.
The findings nevertheless indicate that employers may place a premium on engineers who can combine technical expertise with successful real-world deployment.
For beginners, the priority should be developing strong fundamentals and building practical experience rather than assuming that an FDE title automatically leads to a high salary.
How can Indian engineers prepare for FDE roles?
There is no single career path into forward-deployed engineering. However, software developers, AI engineers and professionals working in solutions engineering may already have some relevant experience.
Aspiring candidates can take several practical steps.
Start with programming fundamentals. Learn a language such as Python or JavaScript, understand APIs and databases, and practise writing maintainable software.
Build an AI-powered application. Develop a project that solves a specific problem instead of creating a basic chatbot without a clear purpose. For example, build a document-search assistant that retrieves relevant information from a collection of files.
Deploy the project. Make the application accessible to users. Learn about hosting, logging, testing, security and monitoring so you can demonstrate more than a local prototype.
Integrate existing systems. Practise connecting your application to databases, APIs and other software. Learn how to handle errors and protect sensitive information.
Improve communication skills. Explain the problem your application solves, the choices you made and the limitations you encountered. Learn to gather requirements and respond to feedback.
Build a project portfolio. Document your work, demonstrate how it operates and explain the results. Practical evidence can help employers evaluate your ability to deliver working solutions.
These steps are a practical preparation strategy, not a guarantee of employment. Candidates should also review job descriptions to identify the skills
