Cloud Solution Architect, Sr.

SUMMARY:

We are seeking highly motivated and detail-oriented Cloud Architect, with 15+ years of experience developing scalable products as well as experience working on enhancements and ongoing maintenance work. Here is an opportunity to be part of developing new products for the next generation of business applications in the Cloud.

EDUCATION & EXPERIENCE:

• Masters of Science in Computer Science or equivalent

• 15+ years of experience developing scalable products as well as experience working on enhancement and ongoing feature and maintenance work.

• Design, create, and monitor daily software build and deployment pipelines ensuring reliability and consistency.

• Collaborate with developers, data scientists, and cross-functional teams to ensure high-quality software and AI-enabled solutions are delivered on time.

• Drive continuous integration, continuous delivery, and continuous deployment (CI/CD) across cloud environments.

• Define and set DevOps and MLOps processes for development, testing, release, and production support.

• Enhance and extend CI/CD automation to improve deployment speed, reliability, and scalability for both traditional and AI-driven applications.

• Lead the integration of AI and ML models into existing systems, ensuring seamless deployment, monitoring, and version control.

• Work with data teams to operationalize machine learning models using AWS SageMaker, Bedrock, or containerized inference services.

• Design and implement AI-powered automation for performance optimization, anomaly detection, and predictive maintenance.

• Provide AI-driven solutions to existing business processes to improve decision-making, efficiency, and customer experience.

• Mentor and guide other development team members on AI integration best practices within DevOps workflows.

• Conduct technical reviews and architecture assessments, ensuring adherence to SDLC, security, and AI governance standards.

• Ensure that unit, integration, and system tests (including AI model validation) are automated per QA requirements.

• Design automation architecture for cloud delivery in AWS, integrating AI and data pipelines where applicable.

• Implement cybersecurity measures and continuously perform vulnerability assessments for cloud and AI workloads.

• Monitor and optimize system performance, model inference speed, and scalability to meet business and technical goals.

• Continuously evaluate new AI tools, frameworks, and cloud services for innovation opportunities.

• Stay current with emerging AI/ML technologies and DevOps trends to recommend improvements.

• Open to taking on additional projects and responsibilities, especially those involving digital transformation and AI innovation.

• Proficient in Maven, Jenkins, SVN, and Git for build automation and version control.

• Strong experience in AWS Cloud platform, including CloudFormation and serverless architecture concepts.

• Expertise in Infrastructure as Code (IaC) using Terraform, AWS CDK, and CloudFormation.

• Experience working with 4-tier architectures in cloud-based deployments.

• Familiarity with software packaging tools (.exe, .deb, .rpm, Docker).

• Experience with relational databases such as MS SQL and Aurora PostgreSQL.

• Strong Linux and Windows administration skills, with hands-on system configuration and automation experience.

• Skilled in CI/CD pipelines using GitLab and Jenkins with automated test and deployment workflows.

• Proficiency in scripting languages (Bash, PowerShell, Python) for automation and AI integration tasks.

• Solid understanding of Docker construction, deployment, execution, and maintenance.

• Experience with containerization and orchestration (ECS, Fargate, Kubernetes).

• Familiarity with AI/ML cloud services such as AWS SageMaker, Bedrock, or Lambda for model deployment and scaling.

• Experience implementing MLOps pipelines for model training, testing, deployment, and monitoring.

• Knowledge of data pipelines, APIs, and microservices used for AI-driven solutions.

• Expertise in AWS CloudWatch and related monitoring services, including AI model and data pipeline observability.

• Strong infrastructure management experience (networks, servers, operating systems, databases).

• Excellent communication and collaboration skills, with experience working in agile, fast-paced, and result-oriented environments.

• Flexibility to assist across functions (e.g., QA, Operations, Data Science).

• AWS DevOps Engineer Professional Certification or equivalent.

• Experience with monitoring and logging tools (CloudWatch, Grafana, Prometheus, SumoLogic).

• Familiarity with AI observability tools (SageMaker Model Monitor, MLflow, or Vertex AI Model Monitoring).

• Understanding of machine learning lifecycle management (MLOps) and model retraining strategies.

• Experience deploying AI APIs or inference services using Docker, Lambda, or ECS.

• Knowledge of cloud cost optimization and GPU resource management for AI workloads.

• Excellent documentation, presentation, and mentoring skills for AI and DevOps architecture.