Sakshi Singh
Seeking full time role | GHC25 Scholar | CS grad at University of California, Davis | Prev at Deloitte, Dell NTT DATA | GenerativeAI, Backend developer, LLM Infra, Cloud Engineering
- Role
- Graduate Software Developer at Uc Davis Electrical And Computer Engineering
- Location
- Davis, CA, US
- LinkedIn followers
- 500 followers
About Sakshi Singh
Hi, I’m Sakshi, a Master’s student in Computer Science at UC Davis who finds purpose in solving complex problems with clarity, compassion, and intention. I’ve always believed that technology becomes truly meaningful when it serves those who often go unheard.I’ve been fortunate to work with amazing teams at NTT Data and Deloitte, building large-scale systems and AI-powered tools across a diverse tech stack: Java, Spring Boot, Microservices, Generative AI, LLMs, Python, React, Azure Cosmos DB, Django, Node.js, AWS and more. Whether I’m architecting backend systems, designing cloud solutions, or working across the stack, I’m driven by the same goal: create something that genuinely helps others.Github- https://github.com/sakshisingh301
Experience
Graduate Software Developer
Uc Davis Electrical And Computer Engineering
Oct 2025 — Present
Led the end-to-end migration of the WeBe Cloud ML Platform from a monolithic service to a microservice-based, asynchronous architecture using Docker, AWS API Gateway, Lambda, and SQS- Designed clear job lifecycle state management to enable observability, traceability, and client-side status polling- Containerized ML services using Docker, enabling deployment across AWS Lambda (container images), ECS Fargate, and EC2- Applied cloud security best practices, including strict input validation, least-privilege access, and secure secrets management- Improved platform scalability and reliability by introducing SQS-based backpressure, isolating failures at the job level, and enabling automatic horizontal scaling via Lambda concurrency- Increased fault tolerance and availability by configuring SQS Dead Letter Queues (DLQs), implementing exponential backoff retry strategies, and optimizing MongoDB connection pooling for high-concurrency Lambda execution- Built end-to-end observability using structured logging with correlation IDs, CloudWatch metrics and alarms, and AWS X-Ray distributed tracing for cross-service request tracing and faster root-cause analysis- Built a CI/CD pipeline for continuous deployment of lambda functions supporting the microservices
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