AWS Cloud DevOps & AI Engineer

Company Description

Giggso, founded in 2017 in Michigan, specializes in helping enterprises manage digital transformation with a focus on AI and governance. As a Minority Business Enterprise (MBE), we provide diverse perspectives for solving intricate challenges and service Fortune 500 clients globally. Our expertise lies in turning AI governance—from a bottleneck into a competitive advantage—by aligning Governance, Risk, Compliance (GRC) with Engineering. Visit us at www.giggso.com to learn more about our mission and initiatives.

Job summary

As a Cloud Application and DevOps Engineer, you will play a critical role in the creation of large-scale, distributed applications that have strong federation, dynamic scaling and configuration, security and policy management, and better resilience and fault tolerance.

Job Responsibilities

• Strong Kubernetes skills including disconnected installation, Kubernetes administration,

and troubleshooting issues with the system

• Extensive hands-on experience on EKS cluster deployment and upgrade in the production environment.
• Extensive EKS troubleshooting experience
• Proven experience in LLMOps, including the deployment, monitoring, and scaling of Generative AI systems in production environments.
• Experience orchestrating and maintaining agentic workflows using frameworks like LangChain, CrewAI, or

AutoGen.

• Hands-on experience managing vector databases (e.g., Pinecone, Weaviate, Milvus) and optimizing RAG

(Retrieval-Augmented Generation) pipelines for low latency.

• Implementation of AI Governance, including security guardrails (e.g., NeMo Guardrails) and cost-management strategies for LLM consumption.
• Experience building Internal Developer Platforms (IDP) for AI, providing shared tooling for model

experimentation and deployment.

• Preferable Experience with OpenShift, AWS, Docker, Kubernetes, Ansible and other container

orchestration/PaaS technologies

• Must have solid Python scripting experience. Automation using boto3, Lambda.
• Strong Linux Administration background with a working knowledge of systems
• Implementation and troubleshooting experience with various virtualization technologies and tools such as KVM
• Experience with the majority of EC2, ELB, EMR, S3 CLI and API scripting
• Strong knowledge of Kubernetes operational building blocks i.e., Kube API, Kube Scheduler, Kube

Controller Manager, ETCD etc

• Providing solutions to common errors, including Create Container ConfigError, ImagePullBackOff,

CrashLoopBackOff and Kubernetes Node Not Ready.

• Extensive knowledge on AWS broad range of services such as provisioning EC2, AMI,
• VPC, ELB, Auto-Scaling, Security Groups, IAM, EBS, AMI, S3, SNS, SQS, Route53, ELB, CloudWatch,

Cloud Formation, Cloud front, Cloud trial, RDS, EMR, Red shift, AWSOpsWork.

• 10+ years of experience with container tools, including Docker, Kubernetes,
• 10+ years of experience in an infrastructure, DevOps, or application development role
• Experience with infrastructure-as-code environments, including activities around the automated server or

network configurations, large-scale software deployments, or monitoring and testing, such as continuous

integration and continuous delivery (CI/CD)

• Experience with using or migrating continuous integration (CI) and continuous delivery.

(CD) pipeline solutions or tools, including Jenkins, Git

• Designed, configured and managed cloud infrastructures utilizing Amazon Web Services including core services EC2, SSN S3, Glacier, Auto Scaling Groups, ELB, EBS, ECS and Database services RDS, DynamoDB, Aurora, Elastic Search and application layer services like API Gateway, Lambda and network layer services like VPC and its subcomponents and Security layers services like IAM, SSM, Cloud trail, Cloud watch, KMS and Integration layer Services like Kinesis, SNS, SQS, Route53 and
• Orchestrated application workflows using AWS Faregate, EKS
• Knowledge of Linux or UNIX administration and automation
• Knowledge of cloud and virtualization-based technologies, including Docker, Azure, Amazon Web Services (AWS)
• Support cloud-hosted systems in a 24x7 environment including troubleshooting, hot

fixing, and root cause analysis

• Configure the cluster for Kubernetes networking, load balancer, pod security and

certificate management

• Configure monitoring tools such Datadog, Dynatrace AppDynamics, ELK, Grafana, Prometheus, or equivalent as required.
• Strong knowledge of Kubernetes application deployment building blocks i.e. Deployments, Services,

Persistent Volumes and Config Maps

• Implement and operate containerized cloud application platform solutions with a focus on application concerns, including cloud-ready, distributed application architectures, migrating workloads to containers, containerized, development workflows, and integrating container platforms with automated continuous integration (CI) and continuous delivery (CD) pipelines.
• The Cloud Infrastructure Engineer will be part of a team of engineers which works on

automation and configuration as code for foundational architecture related to connectivity across Cloud Service Providers. Participate in design reviews of architecture patterns for service/application deployment in cloud (AWS)

Preferred Qualifications

• Bachelor's or master's degree in technical discipline such as Electrical, Electronics or

Computer Science Engineering

• Hands-on experiences with the DevOps and Kubernetes platform
• • Mandatory active certification on AWS Solution Architect and CKA. AI certification will be an add-on.

What We Offer

An exciting opportunity to be part of a growing startup in the cutting-edge field of AI and ML.

A dynamic and inclusive work environment.

Competitive salary with performance-based incentives.

Opportunities for professional growth and development.

Comprehensive benefits package.