AI & Multi-Cloud Architecture Lead

AI & Multi-Cloud Architecture Lead

Role Summary

Responsible for defining and advancing a cloud-agnostic, AI-enabled architecture strategy that supports enterprise analytics, automation, and operational decision-making across multi-cloud environments. This role leads architecture standards and governance across AWS and GCP while actively delivering hands-on prototypes, data pipelines, and AI integrations to accelerate adoption.

Operating as a shared services architecture function, this role both guides and demonstrates best practices—bridging strategy and execution to ensure scalable, cost-efficient, and production-ready solutions aligned with ServiceNow CMDB/APM and Apptio models.

Core Role Identity

Dimension

Expectation

Architecture

Defines standards, patterns, governance

Delivery

Builds POCs, pipelines, and AI integrations

Model

Shared service / enterprise enablement

Authority

Influences + demonstrates (not just advises)

Cloud

Multi-cloud, cloud-agnostic mindset

Key Responsibilities

• 1. Multi-Cloud Architecture & Governance

• Define and implement cloud-agnostic architecture patterns across AWS and GCP

• Standardize GCP governance aligned to AWS controls

• Establish reusable reference architectures for data, AI, and infrastructure

• Promote abstraction via:

• Containers (Kubernetes)

• APIs

• Infrastructure as Code (Terraform)

• 2. Hands-On Enablement (POCs & Pipeline Delivery)

• Build proof-of-concept solutions to validate architecture patterns

• Develop and optimize data pipelines and integrations across systems (ServiceNow, Apptio, Jira)

• Implement AI-enabled workflows (model integration, automation)

• Provide hands-on support to delivery teams to accelerate adoption

• Translate architecture into working, scalable solutions

• 3. AI Integration & MLOps Enablement

• Design and implement AI-ready pipelines (structured + unstructured data)

• Support:

• Model integration into enterprise workflows

• MLOps lifecycle enablement (CI/CD, monitoring, governance)

• AI tool/vendor evaluation

• Mature organization from:

• POCs ? Embedded AI ? Governed enterprise AI

• 4. Data Architecture & Integration (CMDB/APM-Aligned)

• Architect data flows integrating:

• ServiceNow (CMDB/APM)

• Apptio (cost transparency)

• Jira (delivery data)

• Address key challenges:

• Data latency

• Data duplication

• Cost visibility gaps

• Enforce system-of-record and data ownership principles

• 5. Governance & FinOps (Advisory + Enablement)

• Define standards for:

• Cloud cost optimization (FinOps)

• AI governance and lifecycle management

• Data quality and pipeline SLAs

• Support KPI transparency:

• Cloud cost per application

• Data pipeline reliability

• AI ROI

• Guide teams while enabling them through working solutions

• 6. Platform Strategy & Shared Services Leadership

• Act as a central architecture leader and enabler

• Support teams through:

• Architecture reviews

• POC delivery

• Design guidance

• Build reusable enterprise assets:

• Patterns

• Templates

• Integration frameworks

• Required Experience

• 7+ years in cloud architecture, data engineering, or infrastructure

• Proven experience in multi-cloud environments (AWS + GCP)

• Demonstrated ability to:

• Design architecture and deliver working solutions

• Build data pipelines and integrations

• Strong experience with:

• Python, SQL

• ETL/ELT pipelines

• Infrastructure as Code (Terraform preferred)

• Containers (Kubernetes)

• AI & Modern Architecture Requirements

• Hands-on experience with:

• AI/ML integration into enterprise pipelines

• MLOps or AI lifecycle tooling

• Experience evaluating and implementing:

• AI platforms

• Automation tooling

• Preferred Experience

• ServiceNow CMDB/APM integration

• Apptio (cost allocation / FinOps)

• Experience solving:

• Cross-system duplication

• Data lineage challenges

• Exposure to Generative AI integration

• Success Metrics (Aligned to Your KPIs)

• Reduction in cloud cost per application

• Improvement in pipeline SLAs

• Reduction in duplicate data/integrations

• Increase in production AI-enabled workflows

• Adoption of multi-cloud architecture standards

• Number of successful POCs transitioned to production

• The pay range for this role is $150k - $200k per annum including any bonuses or variable pay. Tech Mahindra also offers benefits like medical, vision, dental, life, disability insurance and paid time off (including holidays, parental leave, and sick leave, as required by law). Ask our recruiters for more details on our Benefits package. The exact offer terms will depend on the skill level, educational qualifications, experience, and location of the candidate.