DevOps Engineer

• AWS Cloud Infrastructure: Architect, deploy, and manage secure, scalable cloud infrastructure using AWS services such as EC2, Lambda, S3, DynamoDB, CloudFormation, CDK, EKS, and ECS.
• Infrastructure as Code: Develop and maintain IaC templates and scripts using AWS CloudFormation, AWS CDK, Terraform, or similar tools to automate provisioning and configuration, ensuring reproducibility and consistency across environments.
• CI/CD Pipeline Development: Design, implement, and optimize automated CI/CD pipelines using GitLab CI/CD to enable continuous integration, testing, and deployment of both software and AI/ML models.
• AI Platforms and MLOps: Collaborate with data scientists and ML engineers to deploy, monitor, and maintain machine learning models in production. Build and automate end-to-end ML pipelines, including model training, evaluation, versioning, and deployment using tools such as AWS SageMaker.
• Monitoring and Observability: Implement comprehensive monitoring, logging, and alerting solutions using AWS CloudWatch and New Relic to ensure system health, performance, and security. Proactively detect and resolve issues impacting AI workloads and infrastructure.
• Security and Compliance: Integrate security best practices into all stages of the infrastructure and deployment lifecycle. Manage secrets, enforce access controls, and embed compliance checks into CI/CD pipelines to meet regulatory requirements in the life sciences domain.
• Automation and Optimization: Continuously identify manual processes and bottlenecks to automate using scripting languages (Python, Bash, PowerShell) and AI-driven automation tools, improving efficiency, reliability, and scalability.
• Collaboration: Work cross-functionality with software engineers, data scientists, and product teams to align infrastructure and deployment strategies with business goals and AI innovation.

• Strong expertise in AWS cloud services: EC2, Lambda, S3, DynamoDB, EKS, ECS, CloudFormation, CDK, IAM, CloudWatch.
• Proficiency in Infrastructure as Code tools such as AWS CloudFormation, AWS CDK, Terraform.
• Experience building and maintaining CI/CD pipelines with, GitLab CI/CD, AWS CodePipeline, or GitHub Actions.
• Hands-on experience with AI/ML deployment and MLOps tools like MLflow, Kubeflow, AWS SageMaker.
• Strong scripting skills in Python, Bash, PowerShell, or similar languages for automation.
• Knowledge of monitoring and observability tools such as New Relic or AWS CloudWatch.
• Solid understanding of networking, security best practices, and compliance requirements in cloud environments.
• Excellent communication skills to collaborate effectively across technical and non-technical teams.
• Strong problem-solving mindset with a passion for automation and continuous improvement.
• Adaptability to fast-paced, evolving environments with a focus on innovation.
• Team-oriented approach with a willingness to participate in on-call rotations for production support.

Nice To Have Certifications:

• AWS Certified DevOps Engineer - Professional
• AWS Certified Solutions Architect - Associate or Professional
• AWS Certified Machine Learning - Specialty
• AWS Certified Security - Specialty