AI jobs in India: Head of AI, agentic AI and enterprise AI roles

The AI job market in India now

India's AI job market in 2026 is defined by a gap between demand and experienced supply, and by a shift from experiments to production systems.

Three things stand out. First, the IndiaAI Mission, approved by the Union Cabinet on 7 March 2024 with an outlay of more than ₹10,300 crore over five years and an original target of 10,000 GPUs, has put subsidised compute within reach of startups and researchers, so more Indian employers can build AI rather than only buy it.

Second, agentic AI is early everywhere. McKinsey's State of AI 2025 found 62 per cent of organisations at least experimenting with AI agents but only 23 per cent scaling an agentic system anywhere. The gap between experimenting and scaling is where the five roles below earn their pay.

Third, AI reshapes technology employment as well as adding to it. NITI Aayog's Roadmap for Job Creation in the AI Economy puts India's tech workforce at 7.5 to 8 million, with scenarios ranging from headcount falling to about 6 million by 2031 to up to 4 million new opportunities if industry and government act. The World Economic Forum's Future of Jobs Report 2025 projects 170 million roles created and 92 million displaced globally by 2030. For a candidate, the reading is simple: generic software work faces pressure, and the hiring is in roles that build and run AI systems.

Head of Artificial Intelligence

A Head of AI owns an organisation's applied AI function and is accountable for what AI does in the product or the business, not for research output. The role combines technical direction, team building and commercial judgement about which problems AI should and should not be used on.

What the job actually is. Turning AI strategy into shipped systems: choosing which use cases to fund, build versus buy, evaluation standards and governance, hiring the team, and explaining honest limits to the board. In banks and NBFCs it also means answering to the RBI's FREE-AI committee framework, published in August 2025 with 7 Sutras and 26 recommendations.

What a week looks like. A review of evaluation results on live systems and those about to ship. A model or vendor decision, usually about cost and latency. Two or three hiring conversations. A business head who wants an agent the data cannot yet support. A risk review. Some hands-on time, because credible Heads of AI stay close enough to the code to catch a weak design.

Skills and tools. LLMs and their failure modes, retrieval-augmented generation (RAG), agent frameworks, evaluation design, guardrails, LLMOps and monitoring, and the cloud AI platforms (AWS Bedrock and SageMaker, Azure AI Foundry, Google Vertex AI). Equally important: inference cost modelling, data governance, and a one-page decision memo a CFO will accept.

Typical experience. Usually 12 to 20 years, with at least several years leading ML or data science teams and a clear record of models or AI products running in production at scale.

Who hires it in India. GCCs building AI centres of excellence, large banks and NBFCs, IT services AI practices, SaaS companies and funded AI startups. See also the adjacent fintech Head of Data Science seat.

How candidates are assessed. A structured conversation about two or three systems the candidate shipped: the evaluation, what broke, what changed. Then a strategy case on the employer's own constraints and sessions with the CEO and CTO. Referencing checks whether the systems actually ran and whether teams stayed.

SYTalent estimate, India 2026: ₹1 crore to ₹2.5 crore in annual cash at GCCs and large enterprises; lower cash plus equity at startups. An estimate, not survey data.

Live role: Head of AI, Bengaluru. Apply once, to us. Only shortlisted candidates are contacted.

Enterprise AI Architect

An enterprise AI architect designs how AI fits into a large organisation's systems, data and controls, so that individual use cases share a platform instead of each team building its own. It is an architecture role first and an AI role second.

What the job actually is. Defining the reference architecture for AI across the enterprise: approved models, how data reaches them, where retrieval indexes live, how identity and access control apply to agents, how audit works, and how new use cases are onboarded without new risk. The architect sits between the Head of AI, the CTO's office, security and the business units.

What a week looks like. Architecture reviews for new use cases. Standards for RAG pipelines, vector stores, prompt management and model gateways. Data residency work with information security. A vendor evaluation. Unblocking a team whose design will not pass review.

Skills and tools. Enterprise architecture (integration, APIs, event streaming, identity), data platforms, LLM gateways and model routing, RAG and vector databases, tool-calling security, evaluation and observability tooling, and at least one cloud AI platform in depth. Governance frameworks matter in banking, insurance and healthcare.

Typical experience. Usually 12 to 18 years, often from solution or enterprise architecture, with the last few years spent on data and AI platforms.

Who hires it in India. GCCs of global banks, insurers and manufacturers, Indian banks and NBFCs, IT services firms and large SaaS companies.

How candidates are assessed. A whiteboard design of an enterprise AI platform under real constraints: regulated data, several business units, a legacy integration estate. Interviewers look for trade-off reasoning, security instinct and evidence that past architecture was adopted rather than shelved.

SYTalent estimate, India 2026: ₹60 lakh to ₹1.2 crore in annual cash. An estimate, conservative by design.

Live role: Enterprise AI Architect. Apply once, to us. Only shortlisted candidates are contacted.

Solution Architect (Agentic AI)

A solution architect for agentic AI designs specific agent-based solutions for a customer or business unit and makes sure they can be built, evaluated and run safely. Where the enterprise architect sets the platform, this role designs what runs on it.

What the job actually is. Translating a business process into an agent design: which steps a model should plan and which stay deterministic, which tools the agent can call, what it may do without a human, how state is handled, and how success is measured. In vendors and IT services firms the role is often customer-facing, spanning pre-sales into delivery.

What a week looks like. Discovery workshops. A solution design and evaluation plan. A proof of concept, then an honest account of what production would take. Reviewing agent traces when behaviour drifts. Scoping and pricing.

Skills and tools. Agent frameworks and orchestration patterns (planner and executor designs, multi-agent coordination, tool calling, the Model Context Protocol), RAG, evaluation for multi-step tasks, guardrails and human-in-the-loop controls, tracing, and the cloud AI platforms. The ability to say no to a use case matters as much as technical depth. Our explainer on multi-agent trading systems shows the same design problems in a demanding domain.

Typical experience. Usually 8 to 15 years, from solution architecture, senior engineering or technical pre-sales, with hands-on agent work in the last two years.

Who hires it in India. IT services firms and their AI practices, SaaS and enterprise software companies, AI startups selling to enterprises, and GCCs running internal AI programmes.

How candidates are assessed. A case: here is a process, design the agent, define the evaluation, name the failure modes. Strong candidates volunteer where the agent should not be autonomous. Many employers add a mock client presentation.

SYTalent estimate, India 2026: ₹45 lakh to ₹90 lakh in annual cash, with variable pay common in customer-facing seats. An estimate, not survey data.

Live role: Solution Architect, Agentic AI. Apply once, to us. Only shortlisted candidates are contacted.

Forward Deployed Engineer (Agentic AI)

A forward deployed engineer (FDE) is a software engineer who works directly with customers, inside their environment, to make an AI product actually work on their data and processes. It is an engineering job with customer exposure, not a sales or consulting job.

What the job actually is. Integrating a product into a customer's systems: connecting data sources, building the retrieval layer, writing the tools an agent calls, tuning workflows, setting up evaluation on the customer's real cases, and shipping. FDEs feed what they learn back to the core product team, so the next customer needs less custom work.

What a week looks like. Time with a customer's engineers and operations staff. Integration code in Python or TypeScript. Debugging an agent that fails on a messy document set. An evaluation set built from historical cases. A feedback session with the product team.

Skills and tools. Strong software engineering (APIs, data pipelines, cloud deployment), LLM APIs and agent frameworks, RAG and document processing, evaluation and tracing tools, enough security knowledge to work inside a customer's network, and the judgement to manage scope.

Typical experience. Usually 3 to 10 years of software engineering, ideally including some customer-facing or startup work.

Who hires it in India. AI startups selling to enterprises, SaaS companies with AI products, and AI platform companies serving global customers from Bengaluru. Some GCCs run internal FDE teams that treat business units as customers.

How candidates are assessed. A practical build on an ambiguous problem with messy data, then a mock customer conversation. Employers test whether you ship something useful quickly and explain trade-offs to a non-engineer.

SYTalent estimate, India 2026: ₹30 lakh to ₹75 lakh in annual cash, often with equity at startups. An estimate, conservative by design.

Live role: Forward Deployed Engineers, Bengaluru. Apply once, to us. Only shortlisted candidates are contacted.

AI Engineer

An AI engineer builds applications and systems on top of foundation models: retrieval pipelines, agents, evaluation harnesses and the services around them. The role sits closer to software engineering than to model research.

What the job actually is. Turning a model into a reliable feature: context design, RAG pipelines, tool calling and agent orchestration, structured outputs, caching and cost control, evaluation, monitoring and deployment. Some AI engineers fine-tune smaller models when it improves cost or quality.

What a week looks like. A feature, then the evaluation that proves it works. A regression after a model version change. Latency and token cost work. Production traces. Agreeing with product what the feature does when the model is unsure.

Skills and tools. Python, often TypeScript; LLM APIs; RAG and vector databases; agent frameworks; evaluation sets; guardrails; LLMOps tooling for prompt versioning, tracing and monitoring; containers and a cloud AI platform. ML fundamentals help with fine-tuning and embeddings. Our agentic AI engineer search is a specialised version of this seat.

Typical experience. From 2 to 8 years, usually in software engineering or ML engineering, with recent hands-on work on LLM applications.

Who hires it in India. Every employer type on this page: GCCs, banks and NBFCs, IT services firms, SaaS companies and AI startups.

How candidates are assessed. Coding rounds, a build of a small RAG or agent application, and system design focused on evaluation, failure handling and cost.

SYTalent estimate, India 2026: ₹18 lakh to ₹60 lakh in annual cash depending on experience and employer type. An estimate, not survey data.

Live role: AI Engineer. Apply once, to us. Only shortlisted candidates are contacted.

The five AI roles compared

The five roles share tools but differ in scope, reporting line and accountability. Read the table as a map, not a ladder.

All pay figures are conservative SYTalent estimates of annual cash, not survey data. For AI pay inside financial services, see our fintech salary benchmark.

How to move into these roles

Most people in these roles came from somewhere else, because the roles are only a few years old.

From ML engineering or data science. You have the modelling and evaluation instincts. The gap is usually production discipline: APIs, latency, cost, monitoring and integration. Run an LLM application end to end with a written evaluation and you are a strong AI engineer candidate. Senior data science leaders with production records are the commonest source of Heads of AI.

From software engineering. You already have the hardest part: shipping reliable systems. Learn retrieval, orchestration and evaluation properly, then build something on real data. Backend and platform engineers move well into AI engineer and FDE roles. Our guide on agentic AI trading careers covers the same transition in finance.

From solutions or pre-sales. You have customer instinct and architecture vocabulary. The gap is hands-on credibility. Build proofs of concept yourself and be ready to discuss failure modes in detail. This is the natural route into solution architect roles.

From consulting. You understand processes and change; the gap is technical depth. The route that works runs through an AI strategy or AI product role inside an enterprise or GCC, then a Head of AI seat once you have owned production systems.

Whatever the route, panels want the same evidence: a system you built, how you evaluated it, and what you changed when it failed.

From our desk

These are qualitative observations from the AI searches we run, not statistics.

• Titles do not travel. "AI architect" means a platform role in one company and a pre-sales role in another. Describe what you owned, not your title.
• Evaluation is the dividing line. "How did you know it worked?" separates candidates most reliably. A test set, a caught regression and a rejected metric stand out immediately.
• Demos are discounted. Panels want to hear about the second month in production.
• FDE is misunderstood on both sides. Some engineers avoid it because it sounds like sales; some employers use the title for solutions consultants. The genuine role is one of the fastest ways to build broad AI experience.
• Heads of AI are asked about cost earlier than they expect. Inference spend and the business case come up in the first senior conversation more often than model choice.

Candidates can join the SYTalent network to hear about AI roles that are rarely advertised, or browse all live jobs. Employers hiring for these seats can read about our AI and agentic AI recruitment practice.

Sources

Every figure on this page comes from the sources below. Pay ranges are SYTalent estimates and are not drawn from them.