Gen AI Engineer

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