Rahul Jindal profile photo 1 on Tal

Rahul Jindal

Backend Engineer (Ex-Barclays)| Java · Spring Boot · Python · JS(React/Node)· AWS · Kubernetes · Kafka · Terraform · Docker | GenAI Integration · Agentic AI Workflows | DevOps · Observability | SIH Hackathon Winner

Bengaluru

3+ years of work experience

WEX
Sep 20261 moPresent

Bengaluru

Mobileum
Aug 20251 yr 1 moPresent

Bengaluru, Karnataka, India · Hybrid

• Owned backend services for routing/configuration workflows (tens of millions of events/day); improved production readiness via SLO monitoring, safe rollouts, and incident playbooks. • Optimized Couchbase upsert-heavy APIs by reducing DB round-trips and tuning N1QL queries/indexes; achieved ~50ms typical latency under JMeter load. • Reduced Oracle load and improved tail latency via query/index tuning, batching, and connection-pool right-sizing; validated via before/after baselines and JVM triage (e.g., jstack during spikes). • Hardened customer-facing endpoints using AWS API Gateway + WAF (throttling/filtering) with consistent error contracts; documented via Swagger/OpenAPI to speed integrations. • Built CI/CD with Jenkins + Maven to publish container images and deploy to Kubernetes/OpenShift using Helm (rolling/canary + rollback checks), reducing deployment incidents.
Mobileum
Aug 20232 yrs 1 moAug 2025

Bengaluru, Karnataka, India · Hybrid

• Built ETL pipelines ingesting and aggregating ~250GB/day of telecom logs (~1M logs/min avg) for customer reporting and operational analytics. • Improved analytical performance by shifting workloads from Postgres to ClickHouse/Hive; delivered 3–5× faster queries and more predictable reporting SLAs. • Increased ingestion throughput by optimizing Postgres writes (UPSERT, small-batch inserts), using temp tables for aggregation, and tuning Postgres parameters. • Reduced end-to-end ETL latency by introducing Kafka-based streaming stages using consumer groups, controlled retries + DLQ, and idempotent upserts (at-least-once processing).
Barclays
Mar 20236 mosAug 2023

Bengaluru

• Developed Java backend components for an internal workflow engine with testable business rules and fault-tolerant execution patterns (timeouts/retries). • Improved relational query performance via indexing and query tuning; validated via repeatable benchmarks. • Containerized services using Docker and improved build reproducibility via CI packaging.
GitHub
@rahulj2712
Codeforces
@rahul_81

Rahul’s AI Stack

anthropic apis
anthropic apis
claude code
claude code
github copilot
github copilot
langchain
langchain
mcp
mcp
Pinecone
Pinecone

Education

B. M. S. College of Engineering

Bachelor of Engineering - BE, Computer Science

B. M. S. College of Engineering

20194 yrs2023

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Headline

Backend Engineer (Ex-Barclays)| Java · Spring Boot · Python · JS(React/Node)· AWS · Kubernetes · Kafka · Terraform · Docker | GenAI Integration · Agentic AI Workflows | DevOps · Observability | SIH Hackathon Winner

About

I build the backend layer that doesn't break under pressure. Building distributed systems that handle telecom-scale workloads — 10M+ events/day, 250GB/day ETL pipelines, 50ms p95 latency at production. My work spans high-throughput microservices, Kafka-based event pipelines, Kubernetes deployments, and SLO-driven observability — all battle-tested on Tier-1 telecom infrastructure. I own things end-to-end: from API design and database tuning to CI/CD automation and cost optimization ($15K/month saved via ClickHouse migration). What makes me different right now: I'm at the Backend × GenAI intersection — building agentic AI workflows, integrating LLM APIs into existing microservice architectures, and designing backend infrastructure for RAG pipelines and multi-agent orchestration using Spring AI, LangChain4j, and MCP. As companies go AI-native, I'm building the reliable backend layer those systems run on. Core Stack: Java · Spring Boot · Hibernate · Python · Javascript React · Node.js · Kafka · Kubernetes/OpenShift · AWS · Docker · Terraform · PostgreSQL · ClickHouse · Couchbase · Prometheus/Grafana · CI/CD · OpenAPI AI Layer: Agentic AI · LLM API Integration · MCP · Spring AI · LangChain4j · RAG Pipelines 🎓 B.E. Information Science — BMSCE Bangalore 🏆 Mobileum Achievers Award 2026 | AWS Certified DevOps Engineer – Professional | Oracle Certified Java Developer 📍 Bangalore · Open to SDE-2 / SDE-3 roles at product companies building hard backend or AI-infrastructure problems 📩 Referrals and conversations welcome — DM me directly.

Location

Bengaluru

AI stack

anthropic apis

claude code

github copilot

langchain

mcp

Pinecone

Work experience

Software Engineer 2 at WEX, Sep 2026 - Present · 1 mo, Bengaluru

Software Engineer at Mobileum, Aug 2025 - Present · 1 yr 1 mo, Bengaluru, Karnataka, India · Hybrid, • Owned backend services for routing/configuration workflows (tens of millions of events/day); improved production readiness via SLO monitoring, safe rollouts, and incident playbooks. • Optimized Couchbase upsert-heavy APIs by reducing DB round-trips and tuning N1QL queries/indexes; achieved ~50ms typical latency under JMeter load. • Reduced Oracle load and improved tail latency via query/index tuning, batching, and connection-pool right-sizing; validated via before/after baselines and JVM triage (e.g., jstack during spikes). • Hardened customer-facing endpoints using AWS API Gateway + WAF (throttling/filtering) with consistent error contracts; documented via Swagger/OpenAPI to speed integrations. • Built CI/CD with Jenkins + Maven to publish container images and deploy to Kubernetes/OpenShift using Helm (rolling/canary + rollback checks), reducing deployment incidents.

Associate Software Engineer at Mobileum, Aug 2023 - Aug 2025 · 2 yrs 1 mo, Bengaluru, Karnataka, India · Hybrid, • Built ETL pipelines ingesting and aggregating ~250GB/day of telecom logs (~1M logs/min avg) for customer reporting and operational analytics. • Improved analytical performance by shifting workloads from Postgres to ClickHouse/Hive; delivered 3–5× faster queries and more predictable reporting SLAs. • Increased ingestion throughput by optimizing Postgres writes (UPSERT, small-batch inserts), using temp tables for aggregation, and tuning Postgres parameters. • Reduced end-to-end ETL latency by introducing Kafka-based streaming stages using consumer groups, controlled retries + DLQ, and idempotent upserts (at-least-once processing).

Software Engineer Intern at Barclays, Mar 2023 - Aug 2023 · 6 mos, Bengaluru, • Developed Java backend components for an internal workflow engine with testable business rules and fault-tolerant execution patterns (timeouts/retries). • Improved relational query performance via indexing and query tuning; validated via repeatable benchmarks. • Containerized services using Docker and improved build reproducibility via CI packaging.

Education

Bachelor of Engineering - BE, Computer Science, B. M. S. College of Engineering

Public links

github: https://github.com/rahulj2712

codeforces: https://codeforces.com/profile/rahul_81