Sarthak Agarwal

Software Engineer

Bengaluru, Karnataka, India9 mos experience

Key Highlights

Expert in Java and Spring Boot for microservices.
Developed UI solutions enhancing operational efficiency.
Proficient in data analysis and optimization algorithms.

Stackforce AI infers this person is a Backend-focused Software Engineer with experience in logistics and data-driven solutions.

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Skills

Core Skills

JavaSpring BootReact.jsData Analysis

Other Skills

KafkaFP-Growth algorithm0-1 Knapsack AlgorithmK Nearest Neighbour AlgorithmApache Spark StreamingApache KafkaDataframe APIApache SparkCore JavaJUnitProbabilityWarehouse Management SystemsSpring MVCFront-End DevelopmentReact Native

Experience

9 mos

Total Experience

9 mos

Average Tenure

9 mos

Current Experience

Walmart

SDE 2

Aug 2025 – Present · 9 mos · Bengaluru, Karnataka, India · On-site

Working on microservices for Walmart Catalog Ingestion Systems using Java Spring Boot
Worked on App2App authentication and Kafka topic migration

JavaSpring BootKafka

Walmart global tech india

SDE Summer Intern

May 2024 – Jul 2024 · 2 mos · Bengaluru, Karnataka, India · On-site

Developed an outbound tracker user interface for a live project, enhancing operational efficiency by notifying
fulfillment center associates about order fulfillment statuses and MHE progress. This version of UI was further deployed for production.
Developed an optimized warehouse slotting solution using FP-Growth algorithm, 0-1 Knapsack Algorithm, and
K Nearest Neighbour Algorithm and probabilistic functions to improve item adjacency and placement efficiency,
considering warehouse zone constraints and item-specific features.

React.jsJavaFP-Growth algorithm0-1 Knapsack AlgorithmK Nearest Neighbour Algorithm

Fluxus - iit indore

Video Production and Photography Head

Jan 2024 – Mar 2024 · 2 mos · Indore, Madhya Pradesh, India · On-site

Amazon

Amazon ML Summer School

Sep 2023 – Nov 2023 · 2 mos · Remote

The ML Summer School 2024 included eight modules crafted by Amazon's ML scientists, covering fundamentals to state-of-the-art topics. These modules delved into essential ML concepts such as Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Neural Networks, Sequential Models, Dimensionality Reduction, and Causal Inference.