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Engineering Pods
How We Operate
Hiring AI/ML engineers, cloud engineers, and solutions architects takes months. Retaining them takes even longer. For most growing companies, the gap between what needs to be built and who is available to build it is the single biggest constraint on cloud progress.
Engineering Pods are not staff augmentation. They are dedicated, cross-functional AWS engineering teams that deliver defined outcomes within fixed timelines. Each pod operates as a focused unit with clear scope, accountability, and deliverables.
IDT assembles and operates these pods using the same engineering disciplines we apply across all our services: infrastructure, DevOps, security, data, AI, and application development. Instead of scoping a project and assigning resources, we deploy a ready team that owns a defined problem from day one.
The objective is practical. Get the right engineering capability working on the right problem without the cost, delay, and risk of building an internal team from scratch.
Service Description
Engineering Pods Scope
Each pod is built around a specific operational or engineering challenge. Team composition, duration, and deliverables are defined before work begins. There is no ambiguity about what will be delivered, who will deliver it, or how long it will take.
#ModelTraining
#DevOpsForAI
#MLPipelines
#DataEngineering
#CloudAI
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Our engineering pods cover:
Pods are delivered as fixed-scope engagements or monthly retainers with clear SLAs, defined deliverables, and structured handoff plans. This keeps delivery predictable and avoids the overhead of managing individual contractors, coordinating across vendors, or waiting months for internal hires to ramp up.
- Application development
- DevOps enablement including CI/CD, infrastructure as code, and observability
- Cloud security posture assessment, remediation, and compliance readiness
- Site reliability engineering including SLOs, incident response, and operational frameworks
- Data platform delivery including data lakes, pipelines, and BI layers
- AI infrastructure deployment including LLM hosting, RAG pipelines, and model serving
- AWS cost optimization and FinOps
- AWS environment setup, migration, and landing zone delivery
Our Engineering Pods
#ModelTraining
#DevOpsForAI
#MLPipelines
#DataEngineering
#CloudAI
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AWS Cloud Launchpad Pod
Delivers a secure AWS landing zone and migrates the first production workload. Covers VPC architecture, IAM policies, logging, monitoring, and cost baseline. No internal cloud architect required. The team arrives ready to deliver from week one.
DevOps Delivery Pod
Embeds DevOps capability directly into the engineering organization. CI/CD pipelines, infrastructure as code, observability, and cost optimization are implemented and operated continuously.
- Available in tiered configurations from foundational CI/CD to full-scope DevOps with on-call support and SRE practices
Timelines, team composition, deliverables, and pod configurations are confirmed during the scoping phase of each engagement based on environment complexity, scope of work, and organizational readiness.
Application Development Pod
Designs and delivers cloud-native applications, APIs, and microservices on AWS. Covers serverless architectures using Lambda, containerized workloads on ECS and Fargate, API Gateway integration, and event-driven design patterns.
- Legacy application modernization including re-architecting monoliths into microservices, migrating to serverless, and adopting cloud-native patterns
- Built for teams that need production-grade application delivery without hiring and ramping a dedicated development team
Cloud Security & Compliance Pod
Assesses AWS security posture, remediates gaps, and delivers audit-ready compliance controls. Covers SOC 2, HIPAA, and PCI DSS on AWS-native tooling.
- Compliance deadlines do not move. This pod is built to meet them without adding permanent headcount
SRE Reliability Pod
Implements SLOs, incident response playbooks, chaos engineering, and reliability frameworks on AWS. Targets measurable improvement in MTTR and production incident rates.
- Designed as an upgrade path for organizations that have outgrown basic DevOps but lack dedicated reliability engineering
- Transitions naturally into IDT Managed Observability & Incident Management for ongoing day-to-day operations
Data Platform Pod
Designs and delivers a production-ready data lake, automated pipelines, and BI layer on AWS. Covers S3, Glue, Athena or Redshift, QuickSight, and data governance.
AI Infrastructure Pod
Deploys private, secure AI across cloud, on-premises, or hybrid environments. IDT builds and optimizes the AI solution while collaborating with existing IT teams to host models on local infrastructure where required.
- Cloud AI: Amazon Bedrock integration, RAG pipeline implementation, model serving, AI observability, and governance guardrails
- On-premises AI: Model development and deployment on physical hardware in collaboration with internal IT teams, for organizations with data sovereignty, latency, or regulatory requirements that preclude cloud-hosted AI
- Hybrid AI: Architectures that span on-premises and cloud environments, giving flexibility over where workloads run based on cost, compliance, and performance needs
- AIOps: AI-driven infrastructure operations including predictive alerting, automated incident remediation, intelligent monitoring, and operational optimization across cloud and hybrid environments
Cloud Cost Optimization Pod
Audits AWS spend, identifies waste, and implements cost reduction measures. Covers rightsizing, reserved instances, architecture review, and FinOps framework setup.
- Typically delivers 20–40% AWS cost reduction
- For companies spending $100K+ per month on AWS, this represents 5–10x return on the engagement cost
Managed Delivery Team (MDT) Plus
An embedded pod that owns delivery and operations for a defined AWS domain end-to-end. Architecture, platform operations, incident response, and ongoing improvement are covered under a single agreement.
- One contract replaces a multi-month engineering hiring plan
- Eliminates cloud recruiting overhead for that domain entirely
How we do
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01
Current infrastructure, processes, and gaps are evaluated to determine which pod configuration fits. Work begins from reality, not assumptions, and scoping is completed before the first engineer starts.
Assess the current environment and define the right pod
02
Pods are assembled from experienced AWS engineers who have worked together before. There is no 90-day ramp-up, no recruiting cycle, and no onboarding delay. Teams are operational within one to two weeks.
Deploy a ready team, not a recruiting pipeline
03
Every pod has named deliverables, milestones, and completion criteria. Progress is measurable. If something is not working, it surfaces early because the scope and expectations are explicit from the start.
Deliver defined outcomes with clear accountability
04
Current infrastructure, processes, and gaps are evaluated to determine which pod configuration fits. Work begins from reality, not assumptions, and scoping is completed before the first engineer starts.
Build the path forward, not just the current engagement
Engineering Pods
Hiring AI/ML engineers, cloud engineers, and solutions architects takes months. Retaining them takes even longer. For most growing companies, the gap between what needs to be built and who is available to build it is the single biggest constraint on cloud progress.
Engineering Pods are not staff augmentation. They are dedicated, cross-functional AWS engineering teams that deliver defined outcomes within fixed timelines. Each pod operates as a focused unit with clear scope, accountability, and deliverables.
IDT assembles and operates these pods using the same engineering disciplines we apply across all our services: infrastructure, DevOps, security, data, AI, and application development. Instead of scoping a project and assigning resources, we deploy a ready team that owns a defined problem from day one.
The objective is practical. Get the right engineering capability working on the right problem without the cost, delay, and risk of building an internal team from scratch.
Service Description
Engineering Pods Scope
Timelines, team composition, deliverables, and pod configurations are confirmed during the scoping phase of each engagement based on environment complexity, scope of work, and organizational readiness.
Our engineering pods cover:
-
AWS environment setup, migration, and landing zone delivery
-
Application development
-
DevOps enablement including CI/CD, infrastructure as code, and observability
-
Cloud security posture assessment, remediation, and compliance readiness
-
Data platform delivery including data lakes, pipelines, and BI layers
-
AWS cost optimization and FinOps
-
Site reliability engineering including SLOs, incident response, and operational frameworks
-
AI infrastructure deployment including LLM hosting, RAG pipelines, and model serving
Pods are delivered as fixed-scope engagements or monthly retainers with clear SLAs, defined deliverables, and structured handoff plans. This keeps delivery predictable and avoids the overhead of managing individual contractors, coordinating across vendors, or waiting months for internal hires to ramp up.
Our Engineering Pods
Our managed services are built around clear boundaries, shared ownership, and operational accountability. We take responsibility for critical cloud operations, working closely with internal teams to ensure continuous visibility, security, and reliability across AWS environments.
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Delivers a secure AWS landing zone and migrates the first production workload. Covers VPC architecture, IAM policies, logging, monitoring, and cost baseline. No internal cloud architect required. The team arrives ready to deliver from week one.
.jpg)
Designs and delivers cloud-native applications, APIs, and microservices on AWS. Covers serverless architectures using Lambda, containerized workloads on ECS and Fargate, API Gateway integration, and event-driven design patterns.
- Legacy application modernization including re-architecting monoliths into microservices, migrating to serverless, and adopting cloud-native patterns
- Built for teams that need production-grade application delivery without hiring and ramping a dedicated development team
Implements SLOs, incident response playbooks, chaos engineering, and reliability frameworks on AWS. Targets measurable improvement in MTTR and production incident rates.
- Designed as an upgrade path for organizations that have outgrown basic DevOps but lack dedicated reliability engineering
- Transitions naturally into IDT Managed Observability & Incident Management for ongoing day-to-day operations
_edited.jpg)
Audits AWS spend, identifies waste, and implements cost reduction measures. Covers rightsizing, reserved instances, architecture review, and FinOps framework setup.
- Typically delivers 20–40% AWS cost reduction
- For companies spending $100K+ per month on AWS, this represents 5–10x return on the engagement cost
Deploys private, secure AI across cloud, on-premises, or hybrid environments. IDT builds and optimizes the AI solution while collaborating with existing IT teams to host models on local infrastructure where required.
- Cloud AI: Amazon Bedrock integration, RAG pipeline implementation, model serving, AI observability, and governance guardrails
- On-premises AI: Model development and deployment on physical hardware in collaboration with internal IT teams, for organizations with data sovereignty, latency, or regulatory requirements that preclude cloud-hosted AI
- Hybrid AI: Architectures that span on-premises and cloud environments, giving flexibility over where workloads run based on cost, compliance, and performance needs
- AIOps: AI-driven infrastructure operations including predictive alerting, automated incident remediation, intelligent monitoring, and operational optimization across cloud and hybrid environments
.jpg)
Embeds DevOps capability directly into the engineering organization. CI/CD pipelines, infrastructure as code, observability, and cost optimization are implemented and operated continuously.
- Available in tiered configurations from foundational CI/CD to full-scope DevOps with on-call support and SRE practices
_edited.jpg)
Assesses AWS security posture, remediates gaps, and delivers audit-ready compliance controls. Covers SOC 2, HIPAA, and PCI DSS on AWS-native tooling.
- Compliance deadlines do not move. This pod is built to meet them without adding permanent headcount
_edited.jpg)
Designs and delivers a production-ready data lake, automated pipelines, and BI layer on AWS. Covers S3, Glue, Athena or Redshift, QuickSight, and data governance.
An embedded pod that owns delivery and operations for a defined AWS domain end-to-end. Architecture, platform operations, incident response, and ongoing improvement are covered under a single agreement.
- One contract replaces a multi-month engineering hiring plan
- Eliminates cloud recruiting overhead for that domain entirely
How we do
Assess the current environment and define the right pod
Current infrastructure, processes, and gaps are evaluated to determine which pod configuration fits. Work begins from reality, not assumptions, and scoping is completed before the first engineer starts.
01
Deploy a ready team, not a recruiting pipeline
Pods are assembled from experienced AWS engineers who have worked together before. There is no 90-day ramp-up, no recruiting cycle, and no onboarding delay. Teams are operational within one to two weeks.
02
Deliver defined outcomes with clear accountability
Every pod has named deliverables, milestones, and completion criteria. Progress is measurable. If something is not working, it surfaces early because the scope and expectations are explicit from the start.
03
04
Build the path forward, not just the current engagement
Each pod is designed to connect to the next step. A Cloud Launchpad engagement leads naturally into DevOps Delivery or MDT Plus. A Data Platform pod creates the foundation for AI Infrastructure. The engagement ends, but the architecture and operational model carry forward.