AI/ML ENGINEER, MLOPs, Cloud Security Engineer, DevOps, DevSecOps, SRE, Infrastructure candidate in Richmond, Virginia, United States

Summary

My expertise: RAG workflows, real-time ML inference, multi-cloud MLOps (AWS, Azure, GCP), and Kubernetes-native deployments. I turn complex AI concepts into reliable, revenue-generating systems.

As a DevOps Engineer at Just Do Startup (JDS), I implement and maintain CI/CD pipelines using Azure DevOps Portal and Git, resulting in a reduction of deployment time. I also automate infrastructure provisioning and configuration management using Terraform and Azure CLI, improving system scalability and reducing manual errors.

I have exceptional development ability and extensive knowledge of Azure Cloud Computing Technologies. I have excellent analytical and problem-solving skills and a strong ability to work with minimal supervision. I am passionate about delivering high-quality solutions that meet customer needs and expectations.. Architect and manage multi-cloud environments (Azure & AWS) to support core and ensure high availability, scalability, and disaster recovery.
2. Implement Infrastructure as Code (IaC) using Terraform, AWS CloudFormation, and Azure Resource Manager for automated and consistent deployments.
3. Build and optimize CI/CD pipelines in collaboration with DevOps teams to accelerate secure software delivery.
4. Leverage cloud-native services for data analytics, fraud detection, and AI/ML models using Azure Machine Learning and AWS SageMaker.
5. Enforce zero-trust security models, identity and access management, encryption, and compliance with PCI-DSS, GDPR, and local financial regulations.
6. Monitor and optimize cloud performance and cost efficiency using Azure Monitor, AWS CloudWatch, and custom dashboards.
7. Reduce infrastructure costs through serverless computing and container orchestration (AKS, ECS).
8. Improve system resilience and uptime with hybrid cloud strategies and automated failover mechanisms.
9. Strengthen the banks security posture and compliance readiness across cloud platforms.
10. Monitor and optimize cloud performance and cost efficiency using Azure Monitor, AWS CloudWatch, and custom dashboards.
11. Reduce infrastructure costs through serverless computing and container orchestration (AKS, ECS).Building and maintaining AWS infrastructure following security best practices and baselining.
12. Automated the deployment of cloud Infrastructure with terraform, ansible and jinja2 templates in a multi public cloud environments.

Expectations

I am exploring opportunities to join a forward thinking company to join as Sr/ Staff AI/ML Engineer role where I can design and scale production-grade AI systems leveraging LLMs, RAG architectures and agentic AI for;
> Building and deploying enterprise GenAI systems end-to -end.
> Real-time ML systems handling high volume data.
> optimizing model performance, latency and cost at scale.
>Focused on cloud-native AI systems (AWS, Azure)

AI/ML ENGINEER, MLOPs, Cloud Security Engineer, DevOps, DevSecOps, SRE, Infrastructure

AI/ML ENGINEER, MLOPs, Cloud Security Engineer, DevOps, DevSecOps, SRE, Infrastructure

Summary

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