Cloud & DevOps Engineer | AWS & ML Certified | Expanding into Kubernetes

• 1+ years of hands-on experience building cloud infrastructures with AWS
• AWS Machine Learning certified - now building practical ML skills through hands-on projects
• Actively learning Kubernetes and container orchestration for CKA certification
• Passionate about continuous learning and bridging DevOps with ML operations

I'm a dedicated Cloud & DevOps Engineer currently working full-time, focusing on AWS cloud solutions, implementing CI/CD pipelines, infrastructure-as-code deployments, and containerized applications. With 1+ years of hands-on experience, I'm building my expertise in cloud technologies while maintaining a strong foundation in DevOps practices.

My journey has evolved from traditional cloud infrastructure into exploring Artificial Intelligence and Machine Learning. After earning my AWS Machine Learning certification, I'm now building hands-on experience through practical projects and expanding my skill set into Kubernetes and MLOps practices. I'm currently preparing for the CKA (Certified Kubernetes Administrator) certification while working on ML projects to strengthen my understanding of the full ML lifecycle.

My focus is on gaining practical experience across cloud infrastructure, ML operations, and container orchestration to build well-rounded, end-to-end solutions.

🎯 Next Goal: Certified Kubernetes Administrator (CKA) - Target Q1 2026

AI-powered document search using Amazon Bedrock and OpenSearch

Built an intelligent system that transforms internal documentation into easily searchable knowledge using natural language queries. The solution leverages AWS Bedrock with Titan embeddings for semantic search, OpenSearch Serverless for vector storage, and DeepSeek as the foundation model. Users can ask questions naturally and receive context-aware answers with source links to original Confluence pages.

Tech Stack: Python, AWS (S3, Bedrock, OpenSearch Serverless), Confluence API, React
Impact: Reduced documentation search time from hours to seconds

ML pipeline for analyzing customer feedback sentiment

Building an end-to-end ML pipeline to classify customer feedback sentiment using NLP techniques. The project includes data preprocessing, model training with scikit-learn and TensorFlow, and deployment infrastructure planning. Focus on creating a reproducible pipeline and exploring model versioning and monitoring practices.

Tech Stack: Python, TensorFlow, scikit-learn, Pandas, AWS SageMaker
Learning Goals: End-to-end ML workflow, model evaluation, and deployment strategies

• Sentiment Analysis Project: Building an end-to-end ML pipeline for customer feedback classification
• Kubernetes Learning: Hands-on practice with K8s cluster management, networking, and security
• CKA Certification Prep: Studying for the Certified Kubernetes Administrator exam (Target: Q1 2026)
• MLOps Fundamentals: Learning model deployment, versioning, and monitoring best practices

I'm always interested in collaborating on AWS cloud projects, Kubernetes deployments, and ML learning initiatives. Open to knowledge sharing, learning from experienced engineers, and connecting with fellow tech enthusiasts on similar journeys.

📫 Get in Touch:

• LinkedIn: Connect with me
• Email: contact@silviacosta.io
• Website: cloudswithsilvia.com

💡 "Building expertise in cloud infrastructure, ML operations, and container orchestration - one project at a time."