Java Senior Technical Lead - DevOps, AWS

Job Summary

You’ll build and reproduce hyperscaler infrastructure in-house. The core deliverable is standing up realistic AWS, Azure, and GCP environments—defined entirely as infrastructure-as-code—that clone the setups strategic customers and SIs actually run. Because most field cases originate in hyperscaler environments, faithfully reproducing those environments internally lets the connector and platform teams develop, reproduce issues, and validate against real-world-shaped infrastructure instead of guessing at it.

The environments should include not only core cloud infrastructure, but also the hyperscaler AI services, identity models, APIs, and observability configurations that connector teams need to discover and integrate with.

• Build hyperscaler infrastructure across AWS, Azure, and GCP, defined entirely as code—version-controlled, parameterized, and reproducible.
• Clone customer and SI hyperscaler environments in-house: discover or import their configuration and topology and reproduce an equivalent internally—resource hierarchies, AI services, IAM/auth, and networking. You replicate configuration and shape, not customer data or secrets.
• Configure representative hyperscaler AI services such as AWS Bedrock/SageMaker, Azure AI Foundry/Azure OpenAI, and GCP Vertex AI, including relevant models, deployments, agents, identities, policies, and supporting resources.
• Ensure environments expose realistic APIs, resource relationships, logs, metrics, and audit events so connector teams can develop and validate against representative customer configurations.
• Turn one-off environments into reusable, parameterized IaC modules so any customer topology can be spun up on demand.
• Provide these environments as a stable, refreshable platform the connector team develops and validates against.
• Automate provisioning, refresh, drift detection, and teardown of ephemeral environments.
• Manage cloud cost, quotas, and lifecycle so environments are cheap to create and safe to discard.

• Expert infrastructure-as-code—Terraform (primary), plus CloudFormation, ARM/Bicep, or equivalent.
• Strong multi-cloud infrastructure across at least two of AWS/Azure/GCP (all three preferred): compute, networking, IAM/auth, and resource hierarchies.
• Experience reverse-engineering or importing existing cloud infrastructure into code (e.g., Terraform import, config discovery/scanning tools).
• Deep cloud IAM/auth: roles, service principals, cross-account/tenant/org access, least privilege.
• Hands-on experience with hyperscaler AI platforms such as AWS Bedrock/SageMaker, Azure AI Foundry/Azure OpenAI, or GCP Vertex AI.
• Strong experience working with hyperscaler REST APIs, SDKs, CLIs, and resource inventory/discovery services—not just cloud consoles.
• Experience configuring cloud logging, metrics, and audit services such as CloudWatch/CloudTrail, Azure Monitor/Activity Logs, or GCP Cloud Logging/Audit Logs.
• Automation and scripting in Python, Go, or similar.
• CI/CD for infrastructure and environment-lifecycle management.

• Broader familiarity with hyperscaler AI/ML service families and agentic AI capabilities, including agents, model deployments, knowledge services, guardrails, and AI observability.
• Ephemeral-environment / platform-engineering experience.
• Cloud cost governance for sandbox fleets.
• ServiceNow platform familiarity.

Key Responsibilities

• Build hyperscaler infrastructure across AWS, Azure, and GCP, defined entirely as code—version-controlled, parameterized, and reproducible.
• Clone customer and SI hyperscaler environments in-house: discover or import their configuration and topology and reproduce an equivalent internally—resource hierarchies, AI services, IAM/auth, and networking. You replicate configuration and shape, not customer data or secrets.
• Configure representative hyperscaler AI services such as AWS Bedrock/SageMaker, Azure AI Foundry/Azure OpenAI, and GCP Vertex AI, including relevant models, deployments, agents, identities, policies, and supporting resources.
• Ensure environments expose realistic APIs, resource relationships, logs, metrics, and audit events so connector teams can develop and validate against representative customer configurations.
• Turn one-off environments into reusable, parameterized IaC modules so any customer topology can be spun up on demand.
• Provide these environments as a stable, refreshable platform the connector team develops and validates against.
• Automate provisioning, refresh, drift detection, and teardown of ephemeral environments.
• Manage cloud cost, quotas, and lifecycle so environments are cheap to create and safe to discard.

Skill Requirements

• Expert infrastructure-as-code—Terraform (primary), plus CloudFormation, ARM/Bicep, or equivalent.
• Strong multi-cloud infrastructure across at least two of AWS/Azure/GCP (all three preferred): compute, networking, IAM/auth, and resource hierarchies.
• Experience reverse-engineering or importing existing cloud infrastructure into code (e.g., Terraform import, config discovery/scanning tools).
• Deep cloud IAM/auth: roles, service principals, cross-account/tenant/org access, least privilege.
• Hands-on experience with hyperscaler AI platforms such as AWS Bedrock/SageMaker, Azure AI Foundry/Azure OpenAI, or GCP Vertex AI.
• Strong experience working with hyperscaler REST APIs, SDKs, CLIs, and resource inventory/discovery services—not just cloud consoles.
• Experience configuring cloud logging, metrics, and audit services such as CloudWatch/CloudTrail, Azure Monitor/Activity Logs, or GCP Cloud Logging/Audit Logs.
• Automation and scripting in Python, Go, or similar.
• CI/CD for infrastructure and environment-lifecycle management.

Other Requirements