- 1+ years of hands-on experience building cloud infrastructures with AWS.
- Currently expanding into AI/ML and MLOps to integrate intelligent solutions in cloud environments
- Passionate about continuous learning and implementing modern DevOps practices
I'm a dedicated Cloud & DevOps Engineer currently working full-time, focusing on AWS cloud solutions and learning to implement 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.
I'm on an exciting journey expanding into Artificial Intelligence and Machine Learning, focusing specifically on MLOps practices and AWS AI/ML services. My goal is to bridge the gap between traditional DevOps and modern AI operations while helping companies implement practical AI/ML solutions that solve real business problems.
🎯 Next: AWS Certified Machine Learning Engineer - Associate (Q4 2025)
AI-powered document search and retrieval system for internal documentation
Technical teams waste countless hours searching through scattered Confluence documentation. New joiners struggle to find relevant docs, and even experienced engineers often give up and reinvent solutions or search externally rather than locate existing internal knowledge.
An intelligent document assistant that makes organizational knowledge instantly accessible through natural language queries.
- Data Pipeline: Python script extracts and convert Confluence pages to text files
- Storage: AWS S3 bucket for document storage and versioning
- Knowledge Base: Amazon Bedrock Knowledge Base with Titan embeddings for semantic search
- Vector Database: OpenSearch Serverless for efficient similarity matching
- AI Model: DeepSeek foundation model for response generation and augmentation
- Frontend: ChatGPT/Claude-style interface for intuitive user interaction
Key Features:
- Natural language document search across entire knowledge base
- Source attribution with direct links to original Confluence pages
- Context-aware responses that summarize relevant information
- Quick overview generation to help users decide if they need to dive deeper
Privacy-First Design:
- Documents remain within company AWS environment
- No external model training on proprietary data
- Full audit trail and access control
- Enterprise-grade security and compliance
Tech Stack: Python, AWS (S3, Bedrock (Titan embeddings, DeepSeek (FM)), OpenSearch), Confluence API, Web Frontend
Impact: Transforms hours of frustrating document hunting into seconds of targeted knowledge retrieval.
- Building a Personal ML Portfolio: Developing 3 comprehensive projects showcasing different ML domains
- AWS ML Associate Certification: Studying for certification exam scheduled for Q4 2025
I'm always interested in collaborating on AWS cloud projects, MLOps initiatives, and DevOps automation. Looking for mentorship opportunities in machine learning and open to knowledge sharing with fellow engineers.
📫 Get in Touch:
- LinkedIn: Connect with me
- Email: [email protected]