The AI workforce in 2026 is no longer made up only of machine learning engineers and data scientists. It increasingly includes software developers, data engineers, cloud specialists, product teams, operations professionals, and employees across business functions who use AI as part of everyday work. For global companies, India is emerging as an important market for building these AI-enabled technology teams.
But accessing talent is only part of the challenge. Companies also need to decide which skills they need, where to hire, how employees will be engaged, how human and AI capabilities will work together, and how India-based employees will remain connected to the wider global organization.
What Is an AI Workforce?
An AI workforce is the combination of people, skills, technology, and operating processes that allows an organization to build, deploy, use, supervise, and improve AI-enabled products and business workflows.
An AI workforce therefore extends beyond people who create AI models.
| Workforce area | Example roles |
| AI and machine learning | AI engineers, ML engineers, data scientists |
| Software engineering | Backend, frontend and full-stack developers |
| Data | Data engineers, analysts, data architects |
| Infrastructure | Cloud, DevOps, platform and MLOps specialists |
| Product | Product managers, designers, technical program managers |
| Security and governance | Cybersecurity, governance and risk professionals |
| Business functions | Finance, HR, marketing, operations and sales professionals using AI |
| Leadership | Engineering, technology and business leaders |
This broader definition is increasingly important because AI is moving from a specialist technology into a capability used throughout organizations.
Scaler’s India AI Workforce Report 2026, based on insights from 11,444 professionals, found that nearly one-quarter of learners came from non-technical backgrounds. It also found that almost half of AI-enabled career outcomes were outside traditional engineering roles.
Why Is India Important to the Global AI Workforce in 2026?
India is becoming increasingly important to global AI workforce strategies because it combines a large technology talent base with high levels of workplace AI adoption and growing experience with human-AI collaboration.
Microsoft’s 2026 Work Trend Index offers a useful indication of how quickly this change is happening. It found that:
- 32% of Indian AI users were Frontier Professionals, compared with 16% globally.
- 78% said AI enabled work that was not possible for them a year earlier, versus 58% globally.
- Agent-based workflows were already running at a functional level in 34% of Indian organizations surveyed, compared with 26% globally.
India’s advantage is not simply greater AI usage. The research also points to strong human oversight.
Microsoft found that 63% of Indian respondents prioritized quality control of AI output and 59% identified critical thinking as an increasingly important skill. Eighty-seven percent said they remained responsible for the thinking and treated AI-generated output as a starting point rather than the final answer.
For global businesses, this suggests that India’s opportunity lies in building teams that combine technical capability with judgment, business understanding, and AI fluency.
How Is India’s AI Talent Market Changing?
India’s AI talent market is becoming broader, more geographically distributed, and less dependent on traditional engineering career paths.
Scaler’s 2026 data found that Bengaluru accounted for 19% of the AI talent landscape in its dataset, followed by Pune, Hyderabad, Mumbai, and Chennai. Nearly one in five AI learners came from Tier-II cities such as Lucknow, Jaipur, Patna, Indore, Coimbatore, and Nagpur.
This has two implications for global hiring.
First, companies do not necessarily need to restrict their search to one technology hub. Remote and distributed hiring can broaden access to talent across India.
Second, employers need to look beyond conventional technical credentials. AI is increasingly creating hybrid roles that combine technology with domain expertise.
V3 Staffing’s analysis of the India AI job market similarly highlights growing demand for roles that combine AI exposure with expertise in analytics, finance, operations, customer experience, and other business areas. It also identifies machine learning, generative AI, LLMs, MLOps, and AI automation among areas shaping current hiring demand.
Which Roles Should Companies Hire for an AI Tech Team in India?
The right AI team should be built around the business outcome the company wants to achieve, not around a standard list of AI job titles.
For example, a company building an AI-native product might need:
- Machine learning engineers
- Backend developers
- Data engineers
- MLOps specialists
- Cloud engineers
- Product managers
- Security professionals
A company adding AI to an existing SaaS product might require a different mix:
- Full-stack developers
- API and integration engineers
- AI application engineers
- Data specialists
- Product managers
- QA engineers
Organizations deploying AI internally could instead need operations specialists, data analysts, domain experts, automation professionals, governance specialists, and change-management leaders.
The key question should be:
What work needs to be redesigned, and which combination of humans and AI is best suited to perform it?
What Does an AI-First Team Look Like?
An AI-first team does not simply give employees AI tools. It redesigns roles and workflows so that people and intelligent systems contribute where each is strongest.
EY describes an AI-first workforce as one in which AI is integrated into functions and workflows rather than added as a separate tool. Its research points toward emerging “hybrid pods” in which AI systems perform tasks such as analysis, drafting, and monitoring while people provide oversight, creativity, ethical reasoning, and judgment.
This can change traditional workforce structures.
Instead of measuring workforce capacity only through headcount, companies may increasingly think about the combined capabilities of:
Employees + AI agents + automation + domain expertise
That means hiring strategies need to evolve too.
Companies may need fewer narrowly defined roles and more professionals capable of:
- Working effectively with AI systems
- Evaluating AI-generated output
- Applying domain knowledge
- Solving ambiguous problems
- Collaborating across functions
- Learning new tools continuously
- Taking responsibility for final outcomes
How Can Global Companies Hire Tech Employees in India?
Foreign companies generally have three broad options when building teams in India: establishing their own entity, using genuine independent contractor arrangements where appropriate, or hiring employees through an Employer of Record.
| Hiring model | Often suitable for | Key consideration |
| Indian entity | Larger or established long-term operations | Requires local corporate and employment infrastructure |
| Independent contractor | Genuine independent project work | Actual relationship should reflect contractor status |
| Employer of Record | Employing India-based staff before setting up an entity | EOR serves as the local legal employer |
A business committed to substantial long-term India operations may eventually decide that its own Indian entity is appropriate.
For companies that want to begin hiring employees before reaching that point, an Employer of Record India model offers another structure.
With an EOR, the provider becomes the legal employer in India and manages employment administration, while the client business retains control over employees’ day-to-day responsibilities, performance, and business output. Asanify describes this separation between legal employment responsibility and operational management in its India EOR model.
What Should Global Companies Consider Before Choosing an Employment Model?
The employment model should reflect how employees actually work, the company’s expected India headcount, the roles employees perform, and its longer-term expansion strategy.
For example, contractor arrangements should not be chosen simply because they appear administratively easier. The actual working relationship matters.
Permanent establishment considerations also deserve attention when international companies build India-based teams. An EOR does not automatically eliminate this issue. The activities and authority of the India-based employees can matter, including whether they negotiate contracts, interact with customers, or make commercial decisions.
Companies evaluating these issues can refer to the State of India EOR 2026, which examines EOR structures alongside contractor classification, employment costs, permanent establishment considerations, and the EOR-to-entity decision.
How Should Global Companies Build an India Tech Team?
A scalable India tech team should be built in stages, beginning with business outcomes and ending with an employment and operating model that can evolve as headcount grows.
1. Define what the India team will own
Decide whether employees will build a product, support an existing platform, develop AI capabilities, own a global function, or provide specialist expertise.
2. Map human and AI capabilities
Identify which activities require specialist technical skills, which can be assisted by AI, and where human judgment remains essential.
3. Build a skills mix
Avoid hiring only AI specialists. Strong teams may need software, data, cloud, product, security, and business-domain expertise around them.
4. Broaden the location strategy
Consider India’s wider talent market rather than restricting recruitment to a single technology hub.
5. Decide the employment model before offers are made
Establish whether employees will be hired through an entity, EOR, or another appropriate arrangement.
6. Standardize onboarding
India-based employees should receive the same product context, security standards, company information, and role clarity as colleagues elsewhere.
7. Document how work gets done
Distributed AI teams need clear documentation around technical decisions, AI use, data access, security, model governance, and project ownership.
8. Reassess the structure as the team grows
The workforce and employment model used for the first employees may not remain optimal when the India team becomes larger or more strategically important.
How Can Companies Keep Distributed AI Teams Connected?
AI adoption should improve individual capability without replacing the human interactions that create trust, shared context, and team cohesion.
This becomes particularly relevant in distributed teams.
The Thought Bulb argues that AI itself does not necessarily weaken team cohesion. The risk arises when companies automate activities that previously created useful human interaction without replacing those interactions with new team practices.
For example, AI may reduce the need for:
- Informal draft reviews
- Routine knowledge sharing
- Peer feedback
- Collaborative research
- Repetitive problem-solving sessions
Those efficiencies can be valuable, but managers still need mechanisms that build relationships and shared understanding.
Good practices include:
- Regular technical reviews
- Shared AI experimentation
- Cross-team problem solving
- Manager one-to-ones
- Collaborative learning sessions
- Clear AI-use standards
- Team retrospectives
- Opportunities for informal interaction
The objective should be to make AI a shared organizational capability, rather than a collection of private tools used independently by employees.
Why Human Skills Matter More in an AI Workforce
As AI handles more execution, the value of human judgment, critical thinking, communication, and quality control increases.
Microsoft’s India findings illustrate this clearly. Indian AI users placed particularly high importance on verifying AI output and retaining responsibility for decisions.
EY’s AI-first workforce model reaches a similar conclusion: routine and rules-based work can increasingly move toward AI systems while people concentrate on creative, strategic, and judgment-intensive activities.
For global companies hiring in India, candidate assessment should therefore look beyond technical proficiency.
Useful capabilities include:
- Critical thinking
- Problem-solving
- Communication
- Domain expertise
- AI output evaluation
- Collaboration
- Adaptability
- Product thinking
- Accountability
AI fluency and human capability are becoming complementary rather than competing requirements.
A Practical Model for Building an Initial India Team
Companies entering India without their own entity can combine an EOR employment structure with their existing recruiting, engineering, collaboration, and HR systems.
Asanify is one example of an Employer of Record platform focused on supporting international companies hiring employees in India. Under the model described by Asanify, it acts as the local legal employer and manages areas such as employment agreements, payroll, statutory administration, benefits, and employment documentation while the client organization manages employees’ daily work and performance.
For an AI or technology company, that separation matters. The employer still owns the engineering strategy, technical management, product decisions, security standards, AI governance, career development, and team culture.
An EOR therefore solves an employment infrastructure problem, not the entire workforce-management problem.
As the India team grows, companies should periodically evaluate whether continuing with an EOR or establishing their own local entity better matches their operating requirements.
India AI Tech Team Checklist for 2026
Before building a team in India, global employers should be able to answer:
- What business outcome will the India team own?
- Which roles genuinely require specialist AI expertise?
- Which existing roles need stronger AI capability instead?
- What software, data, cloud, product, and security skills are required?
- Which Indian cities or regions should be included in the talent search?
- Will employees work remotely, hybrid, or in an office?
- What employment model will be used?
- How will payroll and local HR administration be handled?
- What AI tools can employees use?
- What data and security rules apply to those tools?
- Who is accountable for validating AI output?
- How will distributed employees collaborate with global colleagues?
- How will AI skills be developed continuously?
- At what stage should the company reconsider its India operating structure?
Frequently Asked Questions
What is an AI workforce?
An AI workforce combines employees, AI systems, skills, and redesigned workflows. It includes both specialist AI roles and employees across software, product, operations, finance, HR, marketing, and other functions who use AI as part of their work.
Why is India important for global AI hiring?
India combines a large technology workforce with strong workplace AI adoption. Microsoft’s 2026 research found that 32% of Indian AI users qualified as Frontier Professionals, twice the global average in its study.
Is India’s AI talent concentrated only in Bengaluru?
No. Bengaluru remains an important AI hub, but Scaler’s 2026 data also highlights Pune, Hyderabad, Mumbai, Chennai, and growing participation from Tier-II cities.
What roles should a company hire for an AI team in India?
The mix can include AI and machine learning engineers, software developers, data engineers, MLOps specialists, cloud professionals, product managers, security specialists, and domain experts. The correct combination depends on the business outcome the team needs to deliver.
Can a foreign company hire employees in India without setting up an entity?
A foreign company can consider using an Employer of Record, which becomes the local legal employer while the client company continues to manage the employees’ day-to-day work.
Is an EOR the same as outsourcing a tech team?
No. An EOR provides an employment structure. The employees can work directly inside the client’s engineering, product, or business teams rather than providing services through an outsourced technology vendor.
How is AI changing technology jobs in India?
AI is broadening the skills expected in many roles and creating greater demand for professionals who can combine technical or domain expertise with AI capability. Scaler’s 2026 data also shows AI-enabled career outcomes extending well beyond traditional engineering.
What skills matter most in an AI-first workforce?
Technical capability remains important, but critical thinking, judgment, quality control, communication, adaptability, and domain knowledge are becoming increasingly valuable because employees remain responsible for interpreting and validating AI-generated work.
Conclusion
Building an AI workforce in India in 2026 requires more than recruiting AI engineers. Global companies need to design the work itself around the right combination of human expertise, software, data, AI systems, and business knowledge.
India’s workforce is already showing signs of this transition. AI skills are spreading beyond engineering, talent is becoming more geographically distributed, and employees are increasingly working alongside intelligent systems rather than simply using them as standalone tools.
The companies that build sustainable India teams will connect talent strategy, AI adoption, employment structure, team cohesion, skills development, and workforce management from the beginning.
For businesses that want to start hiring before establishing their own Indian entity, an Employer of Record such as Asanify can provide the local employment infrastructure for the initial team. The company can then reassess that structure as its India workforce and long-term operating requirements develop.
