Manaswini Gogineni account profile

Personal details

Manaswini Gogineni

Highlights

DeepLearning.AI Generative AI for Software Development Professional Certificate

Highlights

DeepLearning.AI Generative AI for Software Development Professional Certificate

Work preferences

Desired roles

Back End Developer / Engineer

(Entry-level)

Devops Engineer

(Entry-level)

Front End Developer

(Entry-level)

Data Analyst

(Entry-level)

Data Engineer

(Entry-level)

Additional info

Links & Resume

Github

Experienced in software development, with a strong passion for artificial intelligence and its real-world applications. Specializes in backend systems, cloud infrastructure, and automation, focusing on building scalable solutions and enhancing developer workflows.

Experience

Work history

Software Engineer Intern at Cisco ThousandEyes

(June 2025 - Present)

• Working on the UI development of Integrations Dashboard with the Integrations team under Platform Team. • Development included using Vue.js, Springboot, Java, Javascript

Teaching Assistant at University of Wisconsin-Madison

(August 2023 - May 2025)

• Grading Assignments, Holding Office Hours, Assisting in Course Preparation, Providing Technical Support • Courses: Foundations of Mobile Systems and Applications, Topics in Database Management Systems, Software Engineering

Software Engineer Co-op at Electric Hydrogen

(June 2024 - December 2024)

• Designed a custom Datadog alternative with AWS CDK, Lambda, and Slack Webhooks; improved observability cost efficiency by 30%. • Developed a fail-safe DLQ mechanism for AWS Lambda to S3 pipelines with fine-grained S3 IAM policies. • Optimized PostgreSQL queries and schema indexing using pgAdmin, reducing the slow query count by 35%.

Software Engineer II at J.P. Morgan & Co.

(August 2021 - August 2023)

• Refactored IaC modules using AWS CDK and Terraform to standardize deployments, reducing the provisioning time by 20%. • Improved fault tolerance of Lambda and ECS services via ALB health checks, Step Functions, and dead-letter strategies. • Engineered microservice performance improvements for API Gateway + Lambda stack, lowering cold start latency by 25%. • Real-time dashboards were created using Datadog, Gremlin, and AWS CloudWatch for proactive incident mitigation.