Cloud Engineer in AI

**Hard Skills**

- Cloud Platforms: AWS (S3, Lambda, SageMaker, EC2), Microsoft Azure (ML Studio, Blob Storage, AKS)

- Machine Learning & AI: Model deployment, fine-tuning, and optimization; Deep learning, NLP, Computer Vision

- Containerization & Orchestration: Docker, Kubernetes, Azure Container Instances

- Infrastructure as Code: Terraform, AWS CloudFormation

- Data Pipelines & ETL: Apache Airflow, AWS Glue

- DevOps & CI/CD: Jenkins, GitHub Actions, Azure DevOps

- Security & Compliance: IAM policies, encryption protocols, GDPR

**Soft Skills**

- Analytical thinking and problem solving

- Effective communication in cross-team settings

- Agile methodologies and continuous improvement

- Leadership in project management

- Resilience in fast-evolving tech environments

*Senior Cloud & AI Solutions Engineer*

**InnovateAI Technologies** — San Francisco, CA

June 2022 – Present

- Led the migration of AI workloads to AWS, improving model deployment speed by 40% via serverless architecture

- Architected scalable MLOps pipelines with AWS SageMaker, significantly reducing model training timeframes and enabling continuous deployment

- Designed secure data lakes using S3 and Glue, ensuring compliance with GDPR and HIPAA standards

- Collaborated with data scientists to implement NLP and computer vision models into production environments, resulting in a 25% increase in real-time analytics accuracy

- Mentored junior cloud engineers on cloud security practices and best deployment strategies for AI models

*Cloud Engineer – AI Infrastructure*

**NextGen Data Solutions** — New York, NY

August 2019 – May 2022

- Developed automated CI/CD workflows for ML models using Jenkins and GitHub Actions, decreasing deployment errors by 30%

- Managed multi-cloud environments on Azure and AWS, ensuring high availability and disaster recovery readiness

- Streamlined data ingestion pipelines with Apache Airflow, facilitating real-time data processing for AI applications

- Implemented containerized environments with Docker and Kubernetes, resulting in scalable testing and deployment cycles

- Conducted workshops on cloud security and AI ethics for cross-departmental teams

*Junior Cloud Engineer*

**DataWave Inc.** — Boston, MA

June 2017 – July 2019

- Supported cloud infrastructure setup for AI-driven customer service chatbots, boosting deployment reliability

- Assisted in optimizing cloud storage solutions, reducing costs by 15%

- Developed monitoring dashboards with Azure Monitor and Prometheus for cloud resource utilization

- Provided support in integrating ML models with cloud APIs and frameworks

**Bachelor of Science in Computer Science**

Massachusetts Institute of Technology (MIT)

Graduated: 2017

- AWS Certified Machine Learning – Specialty (2023)

- Microsoft Certified: Azure AI Engineer Associate (2024)

- Certified Kubernetes Administrator (CKA) (2022)

- MLOps Professional Certification (2023)

AI-powered Cloud Optimization Platform

- Developed an AI-driven platform on AWS that predicts optimal resource allocation and dynamically scales cloud resources, reducing operational costs by 20%

- Integrated TensorFlow and PyTorch models within serverless functions, enabling real-time inference at scale

Real-Time NLP Analytics System

- Built an NLP pipeline that processes customer feedback rapidly, providing sentiment analysis with over 92% accuracy and delivering insights to client dashboards

- Leveraged Azure Cognitive Services and custom-trained transformers for improved contextual understanding

- Cloud Platforms: AWS, Azure, GCP

- AI Frameworks: TensorFlow, PyTorch, Hugging Face Transformers

- Orchestration & Containers: Kubernetes, Docker, ACI

- CI/CD & Automation: Jenkins, GitHub Actions, Azure DevOps

- Infrastructure as Code: Terraform, CloudFormation

- Monitoring & Logging: Prometheus, Grafana, Azure Monitor

- Python (advanced)

- Bash scripting

- SQL