AWS DevSecOps Engineer

Dew Software is on the lookout for an AWS DevSecOps Engineer to join our innovative team. As an AWS DevSecOps Engineer, you'll play a vital role in enhancing our security posture while ensuring the efficiency and agility of our cloud operations. Your expertise will help Fortune 500 clients transition seamlessly into the cloud, adhering to best practices for security and performance. At Dew Software, we pride ourselves on driving digital transformation for leading organizations and delivering high-quality solutions that meet the evolving needs of our clients.

Responsibilities

• Design and implement secure cloud infrastructure on AWS while ensuring compliance with industry standards.
• Integrate security practices into continuous integration/continuous deployment (CI/CD) pipelines.
• Automate security monitoring and incident response processes in AWS environments.
• Collaborate with development and operations teams to identify and mitigate security risks.
• Conduct regular security assessments, audits, and vulnerability scans to ensure system integrity.
• Stay updated with the latest security threats and trends, recommending necessary adjustments to policies and procedures.
• Provide guidance and training to teams on DevSecOps best practices and tooling.

Requirements

• Bachelor's degree in Computer Science, Information Systems, or related field.
• 5+ years of experience in DevOps or SecOps roles, with a strong focus on AWS.
• Proven experience with AWS services, including EC2, S3, IAM, and Lambda.
• Solid understanding of security concepts, networking, and compliance frameworks.
• Experience with infrastructure as code (IaC) tools such as Terraform or AWS CloudFormation.
• Proficiency in scripting languages such as Python, Bash, or PowerShell.
• Knowledge of DevSecOps tools like Snyk, Aqua Security, or HashiCorp Vault.
• Strong analytical, problem-solving, and troubleshooting skills.
• Excellent collaboration and communication skills, with a focus on team synergy.

Benefits

• Azure Infrastructure Experience: Proficiency in managing Azure infrastructure components, including virtual machines, storage, and networking, to support AI model development and deployment.
• CI/CD Pipeline Experience: Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines, including the automation of model deployment processes.
• Containerization in the Cloud: Strong knowledge of containerization technologies in the cloud, such as Docker and Kubernetes, for efficient deployment and scaling of machine learning models.
• Machine Learning Expertise: Proficient in building and optimizing machine learning models, with a deep understanding of various ML algorithms and frameworks.
• Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python and libraries like TensorFlow and PyTorch.
• Data Management: Experience in data preprocessing, feature engineering, and data pipeline development for machine learning.
• Collaborative Team Player: Excellent communication skills and the ability to work collaboratively with cross-functional teams, including AI engineers and SREs.
• Documentation: Effective documentation skills to maintain clear and organized records of models, infrastructure configurations, and incident responses.