Lead Consultant | Cloud Platform | Amazon Webservices DevOps

Expedite Talent Solutions; Tampa (FL); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Expedite Talent Solutions in Tampa, FL seeks an experienced AWS DevOps Engineer specializing in Terraform, CI/CD automation, and AI/ML platform deployment. The role focuses on building scalable AWS infrastructure, automating deployments, and enabling MLOps across development to production environments.

Ideal candidates have 8+ years in DevOps/Cloud, hands-on Terraform, Kubernetes, and cloud security, plus experience with AI workloads and SageMaker.

Qualifications

• 8+ years of DevOps/Cloud engineering experience in AWS environments.
• Strong expertise in Terraform and IaC practices.
• Hands-on with CI/CD automation and GitOps workflows.
• Experience deploying AI/ML workloads and ML platforms.
• Proficiency with container orchestration (Kubernetes) and cloud security.

Responsibilities

• Design, deploy, and manage highly available AWS environments.
• Develop and maintain IaC pipelines with Terraform.
• Automate provisioning, configuration, and environment setups.
• Implement governance, security, and cost-optimization measures.

Skills

AWS

Terraform

CI/CD

Kubernetes

Docker

GitHub Actions

Tools

GitHub

Jenkins

AWS CodePipeline

Docker

Job description

Job title: AWS + Terraform with AI

Work Location: Tampa, FL

Vendor Rate: ***/hr

Minimum years of experience: 8+ Yrs

Would you require the candidates to meet you for in person interview? No

Is Skype/WebEx interview,OK? OK

Is this onsite/remote position: Hybrid

If onsite, will you be considering relocation candidates: No

Does this position require Visa independent candidates only? Yes

Job Description

We are looking for an experienced AWS DevOps Engineer with strong expertise in Terraform, CI/CD automation, and AI/ML platform deployment. The ideal candidate will be responsible for building, automating, and managing scalable cloud infrastructure on AWS while enabling AI/ML workloads through robust DevOps practices. This role requires hands-on experience in Infrastructure as Code (IaC), containerization, cloud-native technologies, MLOps, and automation.

Key Responsibilities

Cloud Infrastructure & Automation

• Design, deploy, and manage highly available and secure AWS cloud environments.
• Develop and maintain Infrastructure as Code (IaC) using Terraform.
• Automate cloud provisioning, configuration management, and environment setup.
• Implement cloud governance, security, compliance, and cost optimization strategies.

DevOps & CI/CD

• Design and manage CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or AWS CodePipeline.
• Automate application deployments across development, testing, and production environments.
• Implement GitOps and DevSecOps best practices.
• Manage source control repositories and branching strategies.

AI/ML & MLOps

• Deploy, automate, and manage AI/ML solutions on AWS.
• Support ML lifecycle management, including model training, validation, deployment, and monitoring.
• Work with Amazon SageMaker for model development and deployment.
• Implement MLOps pipelines for continuous model integration and delivery.
• Collaborate with Data Scientists and AI Engineers to operationalize machine learning models.

Containerization & Orchestration

• Build and manage containerized workloads using Docker.
• Deploy and manage Kubernetes clusters using Amazon EKS.
• Implement Helm charts and Kubernetes best practices for scalable deployments.

Monitoring & Security

• Configure monitoring, logging, and alerting using CloudWatch, Prometheus, Grafana, and ELK Stack.
• Implement IAM policies, security controls, secrets management, and vulnerability scanning.
• Monitor infrastructure health and optimize system performance.

Nice-to-Have

• Generative AI deployment experience using Amazon Bedrock, OpenAI, Anthropic, or Hugging Face models.
• Experience with LLM deployment, vector databases, and RAG architectures.
• Knowledge of LangChain, AI Agents, and AI workflow automation.
• Exposure to Data Engineering tools such as Glue, Athena, EMR, or Redshift.
• Experience implementing AI governance and model security frameworks.

Custom Fields:

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Value: true

Name: Intake Call Completed

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