sahishna

Software Engineer – Backend & Distributed Systems

2+ yrs exp 75 · Very Good

About

No bio added yet

Skills & Expertise (28)

Ownership Intermediate

8.0/10

Years Exp

fast learner Intermediate

8.0/10

Years Exp

Lambda Intermediate

7.5/10

Years Exp

AWS SQS Intermediate

7.5/10

Years Exp

Microservices Intermediate

7.5/10

Years Exp

AWS Intermediate

7.5/10

Years Exp

Communication Intermediate

7.0/10

Years Exp

Teamwork Intermediate

7.0/10

Years Exp

CloudWatch Intermediate

6.5/10

Years Exp

GitHub Intermediate

6.5/10

Years Exp

REST APIs Intermediate

6.5/10

Years Exp

SQL Intermediate

6.5/10

Years Exp

Debugging & troubleshooting Intermediate

6.0/10

Years Exp

Pandas Intermediate

6.0/10

Years Exp

NumPy Intermediate

6.0/10

Years Exp

Keras Intermediate

6.0/10

Years Exp

TensorFlow Intermediate

6.0/10

Years Exp

Flask Intermediate

6.0/10

Years Exp

Object-Oriented Programming Intermediate

6.0/10

Years Exp

Data Structures & Algorithms Intermediate

6.0/10

Years Exp

ORM Beginner

5.5/10

Years Exp

Typescript Beginner

5.5/10

Years Exp

Google Cloud Platform Beginner

5.0/10

Years Exp

Azure AI Beginner

5.0/10

Years Exp

Vertex AI Beginner

4.0/10

Years Exp

Java Beginner

4.0/10

Years Exp

Android SDK Beginner

4.0/10

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kotlin Beginner

4.0/10

Years Exp

Work Experience

Software Engineer – Backend & Distributed Systems

Accenture

Aug 2025 - Present

Designed and built scalable, cloud-native Python microservices and integrations in a large distributed computing environment, using AWS SQS + Lambda to ingest 50K+ events/day, powering sub-100 ms analytics pipelines. Adopted GenAI-powered development tools and explored agentic AI workflows to accelerate coding, debugging, and troubleshooting – building familiarity with LLM APIs, prompt design, and AI platform evaluation. Slashed P99 API latency by 42% via PostgreSQL index restructuring, async migration, and resolving N+1 queries – applying data structure and object-oriented design principles to production code. Built resilient, fault-tolerant, cost-effective systems by deploying multi-AZ microservices on AWS EC2 + ALB with auto-scaling groups, achieving 99.9% uptime – cutting incident response time by 60% through CloudWatch monitoring, alerting, and automated rollback triggers. Standardized end-to-end CI/CD pipelines and Git-based deployment workflows, reducing deployment time by 40% while participating in agile ceremonies and on-call rotations.

Research Assistant – Machine Learning Domain

VIT AP University

Feb 2024 - Jun 2025

Processed multi-sensor smartwatch data (heart rate, accelerometer) with data cleaning and normalization. Developed attention-based Convolutional Network models for Human Activity Recognition and continuous heart rate prediction. Achieved 99.8% HAR accuracy and under 2.5 MAE for heart rate prediction across all activity and individual subsets. Optimized model for low to high-intensity activities, enabling adaptive fitness insights across intensities.