Demonstrating Implementation Skills in Data Engineering, AI Engineering, and Infrastructure Management
In the dynamic and ever-evolving field of technology, hands-on experience and the ability to implement cutting-edge solutions are invaluable. As a candidate, I have been actively experimenting with different technologies to address various business use cases, showcasing my skills in data engineering, AI engineering, and infrastructure management. Since I am unable to showcase client projects to demonstrate these skills, I have created an experimental project with a project charter https://github.com/bhaskaraa2/beverage-tech-innovations and backlog https://github.com/users/bhaskaraa2/projects/5/views/1 that I will work on in the coming weeks, based on my workload.
Here is an outline:
Data Engineering
- Technologies considered: Apache Kafka, Apache Spark, Delta Lake, AWS S3 Compatible store
- Objective: To build a real-time data pipeline that ingests, processes, and stores e-commerce data for analytics
AI Engineering
- Technologies considered: Llama, LangChain, LlamaIndex, Python, Docker, Kubernetes
- Objective: To develop a Natural Language Processing (NLP) model that automates customer support interactions, improving response times and customer satisfaction
Infrastructure Management
- Technologies considered: Kubernetes, Prometheus, Grafana
- Objective: To design and implement a scalable and secure cloud infrastructure for a growing startup, ensuring high availability and performance
Project Management
- Currently planning to continue with GitHub projects
My hands-on experience with various technologies and ability to implement solutions that meet business needs make me a strong candidate for roles requiring a blend of technical expertise and practical implementation skills.
I am eager to bring my knowledge and experience to new challenges and contribute to innovative and impactful projects.
Due to different priorities, I am unable to document a timeline for completion, but I will document the process and source code with mock data. The core technical stack has been documented, and I will make minor adjustments during project execution. If you have any suggestions then please share me.