Hiring an enterprise AI architect in India

What the role owns

An enterprise AI architect owns the decisions that every AI team would otherwise make separately and inconsistently. The output is a working reference architecture, not a document set.

The Model Context Protocol matters here because it is becoming the default integration layer. Anthropic introduced it in late 2024 and, on 9 December 2025, donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation co-founded by Anthropic, Block and OpenAI. An enterprise architect now has to decide which internal systems get MCP servers, who approves them and how their permissions are scoped.

How it differs from adjacent roles

The enterprise AI architect works across the whole organisation and sets standards; adjacent roles work inside one programme, one layer or one customer.

The customer-facing counterpart is covered in our guide to the agentic AI solution architect. The Head of AI usually sets the portfolio this architect builds for, and often hires them.

The evidence bar

Credible candidates can name the system, the decision and the result. Ask for three things in the CV and probe each in interview.

• A platform with adopters. For example, an internal LLM gateway on LiteLLM or a cloud API management layer that routes traffic across OpenAI, Anthropic and Google models plus a self-hosted Llama or Qwen model on vLLM, used by a stated number of internal teams. Ask what the gateway enforced: authentication, rate limits, logging, PII redaction, budget caps.
• A build versus buy decision with numbers. For example, choosing Amazon Bedrock or Azure OpenAI over self-hosting for a regulated workload because of data residency and support, or the reverse because of cost per million tokens at sustained volume. Strong candidates quote the cost model, latency targets and what they would revisit.
• Retrieval with access control. Hybrid search (BM25 plus dense vectors) on pgvector, OpenSearch or a dedicated vector database, with document-level permissions inherited from SharePoint, Confluence or the source system. Ask how they measured retrieval quality: recall at k, answer faithfulness, a labelled test set.
• Security that shipped. Threat models against the OWASP Top 10 for LLM Applications and the OWASP Top 10 for Agentic Applications released in December 2025; least-privilege tool scopes; human approval on irreversible actions; red-team findings closed.
• Governance mapped to a framework. A model and use-case inventory, approval workflow and documentation aligned with the NIST AI Risk Management Framework or ISO/IEC 42001.

Red flags

• Architecture described only as diagrams and vendor names, with no system they personally prototyped or debugged.
• Every answer recommends the same vendor, or the candidate cannot say when not to use an LLM at all.
• No cost model: cannot estimate monthly token or GPU spend for a stated workload.
• Security treated as "the InfoSec team's job", with no view on prompt injection through retrieved documents or tool outputs.
• Claims a platform "for the whole company" but cannot name a team that adopted it or one that refused and why.
• Standards set and abandoned: no evaluation harness, so model upgrades went to production untested.

Stack and tools

Assess judgement across layers, not familiarity with every tool. The list below reflects what enterprises in India commonly run in 2026.

Interview loop that works

Run four or five stages over three to four weeks. Senior architects are usually passive candidates and drop out of slow processes.

The practical case. Give a two-page brief: three business units want a customer-service agent, a contract-review assistant and an internal analytics copilot, with customer data that must stay in India and a fixed monthly budget. Ask for a shared architecture, the model and platform choice for each, the evaluation gate before launch, the threat model for one agent with write access, and a monthly cost estimate. Allow 90 minutes plus presentation. Look for reuse across the three, explicit trade-offs and a credible cost number.

Where they come from in India

Very few people in India have held this exact title for long, so source from the seats that produce the skill. Bengaluru has the deepest pool, followed by Hyderabad, Delhi NCR (Gurugram and Noida), Pune and Mumbai.

• Global capability centres. The largest source. GCCs of global banks, insurers and technology companies run AI platform teams that serve many business units, which is this job.
• Product companies and AI-native startups. Platform and staff engineers who built internal LLM gateways and evaluation infrastructure; strong on engineering, sometimes thin on governance.
• IT services and consulting AI practices. Architects who have designed AI platforms for many clients; screen hard for hands-on depth and for ownership after handover.
• BFSI and FinTech. Architects from banks, NBFCs and large FinTechs bring regulated delivery and model risk experience, a scarce combination.
• Cloud providers. Specialist solution architects from hyperscaler AI teams know the platforms well; test for vendor-neutral judgement.

Our AI jobs in India guide maps where these employers sit and what they hire for.

What they cost

Enterprise AI architect pay sits at the top of individual contributor ladders, close to engineering director bands. The figures below are SYTalent Market Data on 2027 annual fixed pay, from our software engineering and AI salaries benchmark, with Bengaluru product companies and GCCs as the reference market.

Fixed pay excludes variable pay, joining bonuses and equity. IT services employers typically pay below these ranges and Big Tech above them; the benchmark sets out the employer and city adjustments. The seat is rarely hired below ten years.

Hiring in GCCs, banks and regulated firms

In regulated firms the architecture is part of the control environment, so the hire must design for audit as well as delivery. Four Indian requirements shape the brief.

• RBI FREE-AI. The RBI's FREE-AI committee report of 13 August 2025 set out 7 Sutras and 26 recommendations, including a board-approved AI policy. It is a committee report, not binding directions, but banks are already building to it.
• Model risk. The RBI's draft Guidance on Regulatory Principles for Model Risk Management, issued on 24 June 2026, covers all models used by regulated entities, including AI and third-party models. The architecture should produce a model inventory, version history and validation evidence as a by-product.
• DPDP Act. The Digital Personal Data Protection Rules, notified in November 2025 with an eighteen-month phased compliance period, make consent, purpose limitation and deletion design questions for any system that sends personal data to a model.
• Data residency. The RBI's payment data storage requirement of 6 April 2018 requires payment system data to be stored only in India, which narrows model hosting choices for payments workloads. Ask candidates how they handled in-country hosting or redaction before external model calls.

GCCs add a parent-company AI policy and cross-border data rules on top. Our guide to AI jobs in fintech and banking covers the wider hiring picture.

How SYTalent runs these searches

SYTalent is a talent search firm working across HFT, FinTech and AI, with 65+ clients and a 50K+ talent network. Our AI, deep tech and quantum practice maps architects by platforms adopted rather than titles held. For a live example, see our Enterprise AI Architect search in Bengaluru. Related guides: forward deployed engineer and the full Roles library. To start a search, brief us.

Sources

Regulatory, framework and protocol references come from the official sources below. Pay ranges are SYTalent Market Data and are not drawn from them. The OWASP Top 10 for Agentic Applications (OWASP GenAI Security Project, 9 December 2025) is credited in plain text.