Yogesh Kumar

Senior Software Engineer

Bengaluru, Karnataka, India3 yrs 11 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in distributed systems and data-intensive platforms.
Led migration to Kubernetes-native architecture, enhancing reliability.
Developed AI-driven tools improving engineering productivity by 40%.

Stackforce AI infers this person is a Backend Engineer specializing in Fintech with expertise in distributed systems and data platforms.

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Skills

Core Skills

Distributed SystemsPythonEvent-driven ArchitectureAwsKubernetesEtlData StorageAi IntegrationSecurity

Other Skills

Data PlatformsAPI ManagementAWS SQSAWS SNSSDK DevelopmentApache AirflowArgo WorkflowsData ProcessingFault ToleranceApache IcebergAWS GlueDuckDBMCPObservability ToolsAI

About

Backend-focused engineer working on distributed systems and data-intensive platforms. Interested in scalability, reliability, and performance at scale. Strong problem-solving background (Codeforces, LeetCode).

Experience

3 yrs 11 mos

Total Experience

2 yrs

Average Tenure

1 yr 11 mos

Current Experience

Arcesium

4 roles

Senior Software Engineer

Promoted

Jan 2026 – Present · 5 mos · On-site

Built a distributed crypto data platform integrating 20+ exchanges, handling 10K+ API calls/min with <1 min SLA
Designed an event-driven scheduling system using SQS/SNS for scalable workflow orchestration
Developed a Python connector SDK with pluggable auth, retries, and adaptive rate limiting

Distributed SystemsData PlatformsAPI ManagementEvent-Driven ArchitecturePython

Software Engineer

Jul 2024 – Jan 2026 · 1 yr 6 mos · On-site

Led the migration of a critical data platform from legacy Apache Airflow to a Kubernetes-native Argo Workflows architecture, significantly improving system reliability and achieving ~40% infrastructure cost reduction.
Designed and owned a high-throughput ETL platform processing 15M+ portfolio position updates per day, with strong guarantees around data correctness, fault tolerance, and horizontal scalability.
Architected distributed data storage using Apache Iceberg on object storage with AWS Glue Catalog and DuckDB as the execution engine, enabling efficient concurrent reads and writes at scale.
Designed and developed MCP (Model Context Protocol) servers to integrate observability platforms (Datadog, Kibana, OpenSearch, Grafana) with AI-powered code agents.
Built and productionized AI-driven developer automation tools, including automated merge request generation and intelligent code review agents, driving a 40% improvement in engineering productivity.
Designed and implemented a cross-account, event-driven scheduling system using AWS SNS and SQS, enabling no-code workflow scheduling across distributed teams.
Implemented Kerberos-based authentication across the MCP ecosystem, improving security, access control, and platform standardization.

KubernetesApache AirflowETLData StorageAI Integration