The AI Platform team is responsible for building and supporting enterprise AI capabilities. They are not building business use cases or AI solutions directly.
Their role is to:
Build and maintain AI platform infrastructure
Enable other teams to consume AI capabilities
Support cloud-based AI services
Establish platform standards and controls
Scale AI capabilities across the enterprise
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Must Haves
Infrastructure Engineering Background
Managers repeatedly emphasized that these candidates need strong infrastructure experience.
Candidates should have experience with:
Cloud platforms
Platform engineering
Infrastructure automation
Infrastructure-as-Code
Enterprise-scale deployments
This is not an application development team.
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AWS Experience
AWS is the primary hiring focus.
Current state:
Team is more mature on Azure
Future state:
Significant AWS expansion planned
Strong AWS experience is highly preferred.
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Infrastructure as Code (IaC)
This was one of the strongest requirements discussed.
Shalini specifically highlighted:
Terraform
Infrastructure provisioning
Infrastructure automation
Candidates should be comfortable:
Writing IaC
Maintaining IaC
Troubleshooting IaC
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Kubernetes
Candidates should have experience with:
Kubernetes
Container orchestration
Examples discussed:
EKS
AKS
Managers indicated Kubernetes experience is important, especially in cloud-native environments.
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Enterprise Communication
This came up multiple times.
Candidates need to:
Participate in technical discussions
Work with multiple teams
Influence without authority
Explain technical concepts
Function in a highly collaborative environment
The AI Platform team supports hundreds of teams across the organization.
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Documentation & Design
Meena emphasized this heavily.
Candidates should be comfortable:
Writing technical documentation
Creating design documents
Supporting architecture reviews
Explaining design decisions
This role is not "just coding."
Documentation is considered an important part of the job.
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Senior-Level Experience
Minimum guidance provided:
5-7+ years of engineering experience
Managers want:
Experienced contributors
Independent problem solvers
Individuals who require minimal hand-holding
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Nice to Haves
Azure Experience
Helpful but not required.
Strong AWS candidates can be successful without extensive Azure experience.
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Generative AI Exposure
Understanding of:
Generative AI concepts
Large Language Models
AI infrastructure
Candidates should understand the ecosystem, but do not need deep expertise building models.
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RAG Framework Knowledge
Shalini referenced:
RAG frameworks
Agentic frameworks
Exposure is beneficial.
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AWS Bedrock
Experience with:
AWS Bedrock
Mentioned as a relevant platform experience.
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OpenSearch / Kendra
Familiarity with:
OpenSearch
Kendra
Considered relevant supporting technologies.
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AgentCore
Awareness or experience is viewed positively.
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OpenAI & Anthropic Ecosystems
Candidates may have worked around:
GPT models
Anthropic models
Managers clarified they are not concerned with specific model expertise.
The focus is platform enablement.
Hiring Manager Concerns / Red Flags
Multiple Jobs / Overemployment
Meena raised concerns around contractors who:
Disappear after standups
Have limited daytime availability
Are potentially supporting multiple full-time jobs
The team has experienced this previously and wants suppliers to screen heavily for it.
Recruiters should validate:
Availability
Engagement
Ability to support core business hours
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Poor Communication Skills
Even strong technical candidates may struggle if they:
Cannot communicate effectively
Cannot work with stakeholders
Cannot document solutions
Communication was repeatedly emphasized.
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Lack of Ownership
The managers want engineers who:
Take initiative
Think at enterprise scale
Drive work independently
Contribute beyond assigned tickets
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