Outdoor Ninja is a mobile app that rewards you for your outdoor discoveries. We have built an AI-based outdoor adventure discovery platform that understands your adventure...more preferences, learns from your past experiences, evaluate your likes, dislikes, inspirations and provides you personalized recommendations for your next set of adventures. Responsibilities: - Designing & coding mobile app in Ionic Framework - Angular JS, HTML, CSS, Firebase, Elastic Search, APIs - Business Dev - Fund Raising
Sr Big Data Consultant - Professional Services
2017 - Present (almost 3 years)
Implementing all big data solutions in AWS cloud for various customers. Technologies heavily used: EMR (Hadoop), Redshift, Elastic Search, Lambda, Glue (ETL), API Gateway,...more Step Functions, Kinesis, S3 (storage) Programming language: Python
- Provide architectural and technical guidance to help customers understand the cloud, and make best use of the Amazon Web Services (AWS) cloud-computing platform to build...more scalable, robust, and secure applications. - Maintain a deep technical understanding of the AWS platform and other cloud and virtualization technologies. - Communicate cloud concepts to both technical and non-technical audiences.
I’m the Founder of Outdoor Ninja. I have over 12 years of experience in Software Engineering, Cloud Computing, Big Data, Machine Learning and Artificial Intelligence. I started working on this idea in mid-2016. For the first 6 months, I attended 2 pre-accelerator programs, validated the problem, researched the market and competition, launched a non-tech MVP, validated and pivoted from a couple of business model strategies, put together a product and MVP strategy and eventually started building our first MVP as a mobile app in Feb 2017. After having built the back-end data engine, coding almost 60% of the mobile app myself, in Jul 2017 I put together a team of interns comprising of 5 developers and 3 UI/UX designers. In Sep 2017, I brought in 3 Data Science interns. Together we worked to put our first machine learning model for the recommendation engine. We have successfully raised our first round of capital through cash crowdfunding and are looking to soft launch our app by Oct 2018.