Vivek Sharma

Backend Developer with 2.5+ years of specialized experience building production-grade, HIPAA-compliant systems for a US-based telehealth platform. Deep expertise in Node.js, TypeScript, and AWS serverless architecture — including Lambda, DynamoDB, API Gateway, SQS, SNS, and Cognito. Proven ability to design event-driven microservices, optimize NoSQL database performance, and implement secure OAuth 2.0 / JWT authentication workflows that meet healthcare compliance standards.

Experienced in Agile/Scrum environments, CI/CD pipelines, and cloud-native backend development. Transitioned into software development from an engineering background (M.E. Robotics), bringing a strong systems-thinking approach to distributed backend design.

Currently pursuing new opportunities after completing a long-term project engagement.

Software Developer - Firminiq System Ltd. - Chandigarh

(2023-04 - 2025-10)

Connected Health Mobile & Cloud Platform - Large-scale healthcare ecosystem supporting BLE medical devices, mobile apps, CMS portals, analytics dashboards on a HIPAA-compliant AWS serverless cloud.

• Designed, developed, and maintained scalable microservices and server-side logic for a US-based telehealth platform, handling high-concurrency workloads using Node.js and TypeScript.
• Built and deployed production-grade RESTful APIs using Node.js, TypeScript, and Express.js, ensuring high availability and reliability for healthcare workflows serving thousands of active users.
• Architected and integrated AWS serverless services — Lambda, DynamoDB, API Gateway, S3, Cognito, SQS, and SNS — to build robust event-driven, cloud-native backend workflows, reducing infrastructure management overhead by ~40% compared to traditional server setups.
• Optimized NoSQL database performance in DynamoDB through efficient schema design using Partition Keys, Sort Keys, and Global Secondary Indexes (GSI), reducing average query latency by 35% and improving read throughput under peak load.
• Automated asynchronous backend workflows using AWS SQS and CloudWatch Events, eliminating manual intervention across 5+ recurring processes and improving system observability and reliability.
• Implemented OAuth 2.0 / JWT authentication and role-based access control (RBAC) via AWS Cognito, contributing to a HIPAA-aligned platform ensuring full data privacy and regulatory compliance.
• Implemented secure user authentication and authorization using JWT tokens with expiry handling, token refresh flows, and AWS Cognito user pools — ensuring session security and preventing unauthorized API access across all healthcare endpoints.
• Wrote and maintained API documentation using Swagger / OpenAPI specifications for internal and external developer teams.
• Participated in code reviews, sprint planning, and retrospectives following Agile/Scrum methodology.

Software Developer - Firminiq System Ltd. - Chandigarh

(2023-04 - 2025-10)

Remote Patient Monitoring (RPM) Platform – VitalSight - Enterprise-grade RPM platform enabling physicians to remotely monitor hypertension patients via EMR integrations and web dashboards.

• Developed Node.js microservices supporting real-time healthcare data processing across multiple regions.
• Integrated clinician EMR systems using secure REST APIs ensuring data integrity and audit logging compliance.
• Built automated alerts and notification pipelines using AWS SNS and SQS for monitoring abnormal patient vitals.
• Supported billing, notifications, and patient enrollment backend workflows with end-to-end encryption.
• Collaborated with product, QA, and compliance teams in Agile/Scrum sprint cycles.

Software Developer - Firminiq System Ltd. - Chandigarh

(2023-04 - 2025-10)

Doctor–Patient Appointment Booking using Agentic AI - Intelligent appointment booking system using Agentic AI to automate doctor–patient scheduling workflows.

• Built an intelligent appointment booking system using Agentic AI to automate multi-step doctor–patient scheduling workflows including slot discovery, booking, rescheduling, and cancellations.
• Integrated LLMs (OpenAI / Hugging Face) for natural language-based patient queries and intent understanding using Prompt Engineering and function calling.
• Implemented Retrieval-Augmented Generation (RAG) using ChromaDB and vector embeddings to fetch doctor availability, specialization, and patient context for accurate responses.
• Developed backend services using Node.js and integrated REST APIs for real-time scheduling and notifications.

M.E. - Robotics - NITTTR, Chandigarh (2020 - 2022-01)

B.Tech - Mechanical Engineering - HPTU, Himachal Pradesh (2011 - 2015-01)