Akshay R K

1. Conserve.AI (EMS Application) Cloud Architecture 

Clients: SRF Dahej, SRF Indore, VECV Bhopal, Tata Motors, Hindustan Unilever

Description: Pioneered the development and implementation of an Energy Management System (EMS) to optimize energy usage, reduce costs, and enhance environmental sustainability. Tech-stack includes Python, JavaScript, Node.js, React.js, and cloud services such as GCP, AWS, Azure Firebase, GCS, S3, DynamoDB, Blob Storage, and Microservices with Docker.

Accomplishments:

· Data Handling and Storage: Aggregated data from diverse sources, stored securely on Google Cloud Storage, AWS(S3), Azure(Blob Storage), ensuring scalability.

· KPI Automation: Automated complex KPI calculations using Python and Node.js on cloud functions, lambda functions, and Azure functions, aiding informed decision-making for factory managers.

· Application Backend with APIs: Led the development of the EMS application backend using Django (Python) and Express (Node.js) with Firebase, DynamoDB, and Cosmos DB.

· Scalability and Flexibility: Engineered a scalable architecture that seamlessly handles increased data volume and additional sources with minimal disruption.

2. Conserve.AI (On-premises Architecture) 

Clients: ITC Bangalore, ITC Pune, Britannia, ITC Saharanpur

Description: Spearheaded the design and development of an on-premises Energy Management System (EMS), eliminating the need for cloud-based infrastructure.

Accomplishments:

· On-Premises Data Storage: Utilized PostgreSQL and MongoDB databases for hybrid data storage, ensuring data integrity, scalability, and efficient handling of structured and unstructured data.

· API Integration: Developed APIs using Python with Django and Node.js with Express for seamless communication between components, enabling dynamic control over energy utilization.

· Task Automation: Incorporated task schedulers for periodic jobs, enhancing operational efficiency and minimizing manual intervention.

· Script-Based Automation: Streamlined repetitive tasks and optimized system performance with Python and Node.js scripts.

· Integration with Apache Server: Integrated the backend with Apache Server for intranet hosting, ensuring seamless access across the organization's local network.

3. Trust.AI Platform 

Client: Hindustan Unilever, STL, BIAL (Bangalore International Airport), JSPL, VECV, Safeway, Tata Motors, Mahindra Motors, Tata Power

Description: Developed a Vision Analytics-Based Application utilizing computer vision and advanced analytics for real-time insights and actionable data across diverse industries.

Accomplishments:

· Platform Development: Designed and implemented the platform using Node.js for the backend and React.js for the frontend, ensuring scalability.

· Backend and Frontend Integration: Seamlessly integrated the backend (Node.js and Python) with the frontend (React.js) for real-time data processing and a responsive user interface.

· Database Integration: Incorporated Firebase/Cosmos DB for secure and efficient data management, enabling the platform to handle large volumes of data.

· Application Configuration: Implemented a flexible application configuration mechanism, allowing users to customize vision-based use cases.

· Alerting Mechanism: Integrated an alerting mechanism for security and operational monitoring, promptly alerting users of unusual events.

· Centralized Camera Monitoring: Enabled centralized monitoring of all connected cameras, streamlining surveillance and quick access to feeds and analytics.

· JWT Token-Based Authentication: Adopted JWT (JSON Web Tokens) for secure and stateless authentication across different platform components.

· Drawing ROI for Images: Developed a feature allowing users to draw Regions of Interest (ROI) on camera images, enhancing analysis and decision-making.