Rahul Rana

Software Engineer

Bengaluru, Karnataka, India6 yrs 8 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in building AI-driven solutions and scalable systems.
Proven track record in optimizing data processing and storage.
Strong leadership in cross-functional project management.

Stackforce AI infers this person is a SaaS expert with a focus on AI and data engineering.

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Skills

Core Skills

Artificial Intelligence (ai)Large Language Models (llm)Big DataApache SparkCloud StorageApache KafkaJavaEtlAmazon Web Services (aws)Spring Boot

Other Skills

AWS GlueC++Cascading Style Sheets (CSS)Cloud DevelopmentDatabase DevelopmentDatabasesDatadogDisaster RecoveryExtract, Transform, Load (ETL)FlaskGoogle BigQueryHTML5HibernateHiveIntersystems Cache

About

I'm a Software Developer with strong experience in distributed systems, data platforms, and AI-driven solutions. At Flipkart, I’m Currently part of a team working on the development of an AI-powered chatbot that uses Natural Language Query (NLQ) and LLMs to simplify data access across the company. Previously at Netomi and EPAM, I built scalable backend systems and data pipelines using AWS, Kafka, and Cassandra, optimising performance and reliability across high-traffic platforms. Skilled in Java, Python, Spring Boot, AWS, Kafka, Spark, SQL/Nosql, and observability tools like Datadog and Splunk. I'm passionate about building smart, scalable systems that deliver real business impact.

Experience

6 yrs 8 mos

Total Experience

2 yrs 2 mos

Average Tenure

4 yrs 1 mo

Current Experience

Flipkart

2 roles

SDE 3

Promoted

Sep 2024 – Present · 1 yr 8 mos · Bengaluru, Karnataka, India · Hybrid

Spearheaded the design and development of an AI-powered chatbot leveraging Natural Language Query (NLQ) to democratize data access, replacing traditional Hive/Spark querying methods and significantly simplifying data retrieval for users.
Exploring and implementing cutting-edge AI techniques, including agents and advanced prompt engineering; rigorously benchmarked various open-source and closed-source Large Language Models (LLMs) to optimise chatbot performance and accuracy.
Leading a team through the full lifecycle of the AI chatbot project, collaborating closely with Product Management, Analysts, and end-customers to define impactful use cases and ensure alignment with business needs.

Artificial Intelligence (AI)Multi-agent SystemsLangChainLarge Language Models (LLM)Vector DBSemantic Search

SDE 2

Apr 2022 – Sep 2024 · 2 yrs 5 mos · Bengaluru, Karnataka, India · Hybrid

Led track to compress and tier ~3PB of data monthly from hot to cold storage, significantly reducing storage costs and enabling cross-zone disaster recovery capabilities.
Enabled Spark SQL as a platform within the data ecosystem, accelerating migration from traditional Hive-based workflows—resulting in a 70% reduction in average job runtimes (from 10 to 3 hours) and 35% cost savings; also collaborated with the Google team to integrate BigQuery, achieving up to 3x faster job executions.
Drove enhancements in system reliability and data governance by establishing monitoring for rogue job metrics, implementing a system stabilisation handler, and automating PII data handling processes to meet security and compliance standards.

JavaPython (Programming Language)Big DataHiveApache SparkGoogle BigQuery+3

Netomi

SDE 1

Apr 2021 – Apr 2022 · 1 yr · Gurugram, Haryana, India

Implemented rate limiting for AI Studio to flatten traffic spikes, reducing DataDog-to-Slack alert costs and cutting DevOps on-call time by 30+ minutes.
Developed a Kafka Connect–based chat transcript feature that eliminated 30+ Java classes, 1,500+ lines of code, and reduced Cassandra load, while collaborating with an 8-member agile team to design and deliver customer-driven feature enhancements.

JavaApache KafkaDatadogRedisWorkatoTeam Building+1

Epam systems

2 roles

Software Engineer

Promoted

Jul 2020 – Apr 2021 · 9 mos · Hyderabad, Telangana, India

Developed and optimized scalable ETL pipelines using AWS Glue, Athena, S3, and Scala—reducing EOD data processing time from 2 hours to 1.2 hours for ~200GB of data—while also building RESTful APIs for cross-team data access and stabilizing ingestion workflows to enhance overall system reliability.

JavaSpring BootExtract, Transform, Load (ETL)Amazon Web Services (AWS)PostgreSQLETL

Junior Software Engineer

Sep 2019 – Jul 2020 · 10 mos · Hyderabad, Telangana, India

Designed and developed RESTful APIs to support Front-End, Analytics, and cross-functional teams, enabling efficient data retrieval from PostgreSQL with business-specific computations.
Implemented database version control for PostgreSQL and InterSystems IRIS using Liquibase, facilitating seamless collaboration among 30+ developers across various teams and regions.

JavaSpring BootAmazon Web Services (AWS)LiquibaselogdnaIntersystems Cache