A Generative Artificial Intelligence Engineer develops, designs, and manages generative models and algorithms that support the generation of new content in the form of images, text, audio, and other multimedia. They utilize GPTs, GANs, VAEs, and other deep learning architectures to craft systems capable of generating data. May work with data scientists, machine learning engineers, and software developers.
4101392
Job Summary
Job title : AI & Multi-Cloud Architecture Lead
Job Location :
Palm Bach Florida , (Onsite )
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.
Tech Mahindra is an Equal Employment Opportunity employer. We promote and support a diverse workforce at all levels of the company. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin, or disability. All applicants will be evaluated solely on the basis of their ability, competence, and performance of the essential functions of their positions with or without reasonable accommodations. Reasonable accommodations also are available in the hiring process for applicants with disabilities. Candidates can request a reasonable accommodation by contacting the company ADA Coordinator at ADA_Accomodations@TechMahindra.com
