Kartik Jhanwar

AI Researcher

Bengaluru, Karnataka, India3 yrs 10 mos experience

Key Highlights

Optimized pipelines reducing runtimes by ~85%
Designed failure recovery system improving resilience by ~75%
Strong foundation in operating systems and algorithms

Stackforce AI infers this person is a Backend Engineer specializing in Fintech and Distributed Systems.

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Skills

Core Skills

Distributed SystemsBackend EngineeringContinuous Integration And Continuous Delivery (ci/cd)

Other Skills

DockerObject Oriented DesignApache KafkaAmazon Web Services (AWS)JavaFastAPITypeScriptSQLRedisGitFlaskBashElasticsearchREST APIsJavaScript

About

Backend-focused Software Engineer with 2+ years of experience building and scaling event-driven, distributed systems in production. I’ve worked extensively on asynchronous workflows, failure recovery, batch processing, and release reliability across Kafka, Redis, and Elasticsearch backed platforms. My work includes optimizing long-running pipelines (up to ~85% runtime reduction), building job replay and recovery mechanisms, strengthening CI/E2E testing, and developing internal workflow tooling to improve execution efficiency and system reliability. I’m a Computer Science graduate from IIT Kanpur (2023, GPA 8.3) with a strong foundation in operating systems, algorithms, and systems programming, and hands-on project experience in OS internals, large-scale plagiarism detection, and ML-based gesture recognition. I’m interested in roles focused on backend engineering, distributed systems, system design, and building scalable, reliable platforms.

Experience

3 yrs 10 mos

Total Experience

Average Tenure

Current Experience

Javis

Agentic AI Engineer

Jan 2026 – Present · 5 mos

DockerObject Oriented DesignApache KafkaAmazon Web Services (AWS)JavaFastAPI+16

Goldman sachs

2 roles

Software Engineer

Promoted

Jul 2023 – Jan 2026 · 2 yrs 6 mos · Bengaluru, Karnataka, India · On-site

Built and scaled event-driven backend services for high-throughput asynchronous workflows using Python, Java, Kafka, Redis, and Elasticsearch in production.
Designed a failure recovery and replay system for distributed jobs, cutting manual operational recovery by ~75% and improving system resilience.
Optimized long-running batch and reporting pipelines using parallel execution and bucketing with S3 reads, reducing runtimes by ~85% (hours → sub-hour).
Improved release reliability and execution efficiency by strengthening Dockerized E2E testing and developing internal workflow tooling and operational UIs.

Summer Analyst

May 2022 – Jul 2022 · 2 mos · Bengaluru, Karnataka, India · On-site

Re-engineered a serial release-validation pipeline into a parallel execution system, reducing end-to-end checkout time from ~2 days to ~3–4 hours.
Built a continuous diff-testing and debugging framework to isolate breaking changes to ~5-minute windows, significantly reducing release investigation time.