Technical Architect

Michael Page; India; On-site; INR 4,000,000 - 8,000,000; Full time

Benefits offered by this job

Competitive salary package

AI led projects

Growth opportunities

Job summary

Michael Page is recruiting an senior AI platform architect to lead enterprise AI platform design, including services, APIs, data stores, security, and deployment models. You will shape scalable, multi-tenant, cloud-native solutions across Azure, AWS or GCP, ensuring governance and regulatory alignment.

You will collaborate with product and engineering leaders to align architecture with roadmaps and client expectations, while guiding pre-sales, RFPs, and technology evaluations through

Qualifications

• 12-16 years in software engineering, including 5+ years in solution or platform architecture.
• Large-scale distributed systems designed and shipped, with experience of scale constraints.
• Deep microservices and API architecture: contracts, versioning and integration patterns.
• Strong cloud architecture on Azure, AWS or GCP, with containerisation and Kubernetes in production.
• DevSecOps fluency: security by design, secrets management, supply chain and CI/CD pipelines.
• Non-functional rigour: performance, availability, observability, cost and compliance.
• Decision discipline - able to write and defend an architecture decision record, not just a diagram.

Responsibilities

• Define target architecture for enterprise AI platforms: services, APIs, data stores, security model, deployment topology.
• Produce solution blueprints for engineering and client validation.
• Design for scale, resilience and multi-tenancy across distributed, cloud-native systems.
• Architect the AI layer including LLM/RAG serving, agent orchestration, vector stores, and model-deterministic boundaries.
• Define AI infrastructure patterns: latency, cost, routing, caching, observability.
• Set data-handling architecture for deployable AI in regulated environments.
• Define integration patterns with enterprise platforms and data pipelines, and lead governance across teams.
• Drive technology evaluations, POCs and build-versus-buy decisions.

Skills

Enterprise architecture

Cloud architecture

Microservices

API design

Security by design

Decision discipline

Tools

Azure

AWS

GCP

Kubernetes

CI/CD

Job description

• Opportunity to be a part of a global consulting company
• Competitive salary and bonuses

About Our Client

Our client is a global AI and data transformation specialist, helping companies turn digital investment into sustainable business impact by delivering ROI from AI@Scale.

Job Description

1. Architecture and blueprints

• Define target architecture for enterprise AI platforms: services, APIs, data stores, security model, deployment topology.
• Produce solution blueprints that engineering can build from and that clients can assure against.
• Design for scale, resilience and multi-tenancy across distributed, cloud-native systems.

2. AI platform architecture

• Architect the AI layer - LLM and RAG serving, agent orchestration, vector stores, and the line between model-driven and deterministic behaviour.
• Define AI infrastructure patterns: inference cost and latency, model routing, caching, evaluation hooks, observability.
• Set the guardrail and data-handling architecture that makes AI deployable in regulated environments.

3. Integration and governance

• Define integration patterns with enterprise platforms - identity, core systems, data pipelines, event backbones.
• Lead architecture governance: review forums, standards, technical debt calls and architecture compliance across teams.
• Drive technology evaluations, POCs and build-versus-buy calls on evidence rather than preference.

4. Engagement and influence

• Partner with Product and Engineering leadership to keep architecture aligned to roadmap and delivery reality.
• Represent the architecture to client CTOs, security teams and auditors, and support pre-sales and RFPs.

The Successful Applicant

• 12-16 years in software engineering, including 5+ years in solution or platform architecture.
• Large-scale distributed systems designed and shipped - with the scars to show what fails at scale.
• Deep microservices and API architecture: contracts, versioning and integration patterns.
• Strong cloud architecture on Azure, AWS or GCP, with containerisation and Kubernetes in production.
• DevSecOps fluency: security by design, secrets management, supply chain and CI/CD pipelines.
• Non-functional rigour: performance, availability, observability, cost and compliance.
• Decision discipline - able to write and defend an architecture decision record, not just a diagram.

What's on Offer

• Competitive salary package.
• Opportunities to work on innovative AI led projects
• Professional growth and learning opportunities.