Bhavesh Khandelwal

Senior Software Engineer

Bengaluru, Karnataka, India4 yrs 8 mos experience

Key Highlights

Expert in building and scaling distributed systems.
Achieved 30% database performance improvement.
Reduced QA workload by 60% with automation tools.

Stackforce AI infers this person is a Backend-heavy Fullstack Engineer in Fintech.

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Skills

Core Skills

FlaskMicroservicesSqlAutomation ToolsPythonAws

Other Skills

AdaptabilityAmazon Relational Database Service (RDS)Amazon S3Amazon Web Services (AWS)AnsibleArgoCloud ComputingDockerExcellent communication skillsGitGo (Programming Language)JavaScriptMongoDBMySQLNode.js

About

Sr. Software Engineer with expertise in building, and scaling distributed systems and backend platforms across financial services — powering digital lending, payments, and wealth management products.

Experience

Dezerv

Senior Software Engineer

Aug 2024 – Present · 1 yr 7 mos · Bengaluru, Karnataka, India · On-site

Kreditbee

Senior Software Engineer

Jun 2023 – Jul 2024 · 1 yr 1 mo · Bangalore Urban, Karnataka, India · On-site

Developed and integrated secure web applications using Flask and REST API structures.
Implemented microservices dedicated to rule-based decisions, enhancing overall development lifecycle.
Optimized SQL queries and stored procedures, resulting in a 30% improvement in database performance.

FlaskREST APIsSQLMicroservices

Telus international ai data solutions

2 roles

Senior Software Engineer | Playment - Telus International AI Computer Vision

Jan 2023 – Jun 2023 · 5 mos · Bengaluru, Karnataka, India · On-site

Developed an internal Smart QC tool, reducing Quality Analysts' workload by 60% and enhancing data quality by 20%.
Built automation tools for annotation tools, cutting QA workload by 25% and reducing new annotator onboarding time.

Smart QC toolAutomation tools

Software Engineer | Playment - Telus International AI Computer Vision

Jun 2021 – Jan 2023 · 1 yr 7 mos · Bengaluru, Karnataka, India · On-site

Built internal automation tools using Python, PostgreSQL, AWS, and Retool, optimizing training for annotation tools.
Implemented intensity capping feature for a tool using Python, AWS, Redis, and PostgreSQL, improving annotator work efficiency.
Established a monitoring and alerting system, leading to a 20% decrease in resource idle time.

PythonPostgreSQLAWSRetoolRedis