✦ Featured Snippet — Overview
Arindam Vashishth — Backend Software Engineer at Wells Fargo
Backend Software Engineer at Wells Fargo (Hyderabad) with about 3 years of experience designing scalable microservices, high-throughput event-driven pipelines, and distributed systems. B.Tech graduate from IIT Kanpur (2023). Specializes in Java, Spring Boot, Kafka, and Apache Ignite cluster optimizations, as well as AI/ML model development.
JavaPythonSpring Boot
Apache KafkaApache IgniteAkka Actor Model
REST APIsMicroservicesSplunk
PyTorchScikit-LearnDistributed Systems
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wellsfargo.com › careers › engineering › arindam-vashishth
Wells Fargo – Software Engineer, Hyderabad · Aug 2023 – Present
Reduced operational costs by delivering an in-house batch scheduling framework replacing Autosys using Java, Spring Boot, and Akka, scaling to 1,000+ daily jobs. Architected a fault-tolerant Distributed Snapshotting Mechanism using Apache Ignite for 500GB+ daily volume. Migrated replication framework to Apache Kafka, handling 80M+ daily records and cutting incident rates by 90%.
Reduced operational costs by delivering an in-house batch scheduling framework replacing Autosys using Java, Spring Boot, and Akka, scaling to 1,000+ daily jobs. Architected a fault-tolerant Distributed Snapshotting Mechanism using Apache Ignite for 500GB+ daily volume. Migrated replication framework to Apache Kafka, handling 80M+ daily records and cutting incident rates by 90%.
Wells Fargo ExperienceJava, Spring Boot, Akka scheduler
Ignite Distributed Cache500GB+ volume snapshotting
Kafka Data Streams80M+ daily records & 3x throughput
Siemens Data ScienceGNN load flow prediction model
People also ask
What did Arindam accomplish at Wells Fargo?expand_more
Arindam designed and delivered a custom in-house batch scheduling framework using Java, Spring Boot, and Akka that scales to 1,000+ daily jobs across 20+ teams, replacing proprietary Autosys software. He built an Apache Ignite-based distributed snapshotting mechanism to secure 500GB+ daily volume, migrated legacy replication streams to a high-throughput Apache Kafka system processing 80M+ daily records (reducing incidents by 90%), and built Splunk observability dashboards across 100+ hosts, reducing MTTR by 20%.
What is his technical background and education?expand_more
Arindam holds a Bachelor of Technology (B.Tech) from the Indian Institute of Technology Kanpur (IIT Kanpur), graduating in July 2023 with a CGPA of 8.3/10. His primary focus is on backend engineering, distributed systems, event-driven pipelines, and machine learning.
What machine learning projects has he developed?expand_more
Reinforcement Learning Trading Agent: Built a Deep Q-Network (DQN) trading agent in PyTorch achieving a Sharpe Ratio of 1.4 and 12% annualized return on equity portfolios.
Music Generation: Trained polyphonic LSTMs on a 55,000-song dataset with 85% note prediction accuracy using truncated BPTT.
Siemens Grid Load Flow: Built Graph Neural Networks (GNN) in PyTorch Geometric to predict load flow solutions with 0.08 MAE.
Music Generation: Trained polyphonic LSTMs on a 55,000-song dataset with 85% note prediction accuracy using truncated BPTT.
Siemens Grid Load Flow: Built Graph Neural Networks (GNN) in PyTorch Geometric to predict load flow solutions with 0.08 MAE.
How can I contact Arindam Vashishth?expand_more
You can reach Arindam at vashishtharindam@gmail.com or call +91-8847605158. His LinkedIn is linkedin.com/in/arindamv and GitHub is github.com/arindamv. He is based in Hyderabad, India.
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github.com › arindamv › projects
Flagship engineering and research projects specializing in deep learning, reinforcement learning, and advanced architectures.
Stock Prediction & Reinforcement Learning
Python · PyTorch · DQN · Scikit-Learn · MPT
Reinforcement Learning trading agent using Deep Q-Network, policy gradients, and MPT. Achieved a Sharpe Ratio of 1.4 and 12% annualized return on equity portfolios.
Music Generation using Deep Learning
Python · PyTorch · LSTM · RNN · Kaggle Dataset
Trained character-level RNNs and polyphonic LSTMs on a 55,000-song dataset, achieving 85% note prediction accuracy. Reduced memory by 40% using truncated BPTT.
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iitk.ac.in › departments › me › arindam-vashishth
Indian Institute of Technology Kanpur (IIT Kanpur) · Bachelor of Technology (B.Tech) in Mechanical Engineering · Class of 2023 · CGPA: 8.3/10.
Secured top ranks in national-level competitive examinations (JEE Advanced and Mains) and completed case study challenges.
Secured top ranks in national-level competitive examinations (JEE Advanced and Mains) and completed case study challenges.
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github.com › arindamv › resume › Arindam_Vashishth_Resume.pdf
Download the comprehensive resume detailing 3 years of software engineering at Wells Fargo, data science intern at Siemens, projects, academic honors, and technical skills.
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💼 Work Experience
Backend Software Engineer · Distributed Systems · Event-Driven Pipelines
Aug 2023 – Present
Wells Fargo
Hyderabad, India
Current Role
💛 Wells Fargo
Software Engineer
📍 Hyderabad, India
Aug 2023 – Present
- Reduced operational costs by designing and delivering an in-house batch scheduling framework using Java, Spring Boot, and Akka Actor Model to replace third-party Autosys software, scaling to 1,000+ daily jobs and standardizing REST API-driven execution workflows across 20+ teams.
- Architected a fault-tolerant Distributed Snapshotting Mechanism using Apache Ignite to eliminate cache eviction failures and ensure data consistency across 500GB+ daily volume, improving intraday and EOD reporting durability for 5+ critical reporting pipelines.
- Led end-to-end migration of a legacy replication framework to a resilient Apache Kafka-based system, processing 80M+ records daily with custom producers and consumers, cutting replication incidents by 90% and improving throughput by 3x.
- Built centralized Splunk observability dashboards across 100+ distributed server hosts, consolidating JVM heap, uptime, API hit, and latency metrics from 3+ environments, reducing MTTR for production incidents by 20% and eliminating manual log aggregation.
- Optimized Apache Ignite cluster topology and database configuration, reducing system downtime by 30% and improving transaction throughput across 10+ microservices. Enforced enterprise-grade access control using Kerberos authentication and authorization.
Previous Experience
💙 Siemens
Data Science Intern
📍 Remote
May 2022 – July 2022
- Built a machine learning regression model using PyTorch Geometric and Graph Neural Networks (GNN) to predict Load Flow solutions in power grids across 50+ network configurations, achieving MAE of 0.08 and MSE of 0.04.
- Automated GNN hyperparameter tuning via Grid Search across key parameters including learning rate, hidden layer dimensions, and dropout rates, cutting tuning time by 30% and identifying the optimal model configuration for voltage prediction accuracy.
Career Timeline
Wells Fargo – Software Engineer
Designing scalable microservices, Akka actor scheduling engines, distributed Apache Ignite cache snapshots, and Kafka event pipelines.
Siemens – Data Science Intern
Built Graph Neural Networks (GNNs) for power grid load flow predictions. Developed automated hyperparameter search scripts.
IIT Kanpur – B.Tech in Mechanical Engineering
Studied core engineering and algorithms, graduating with a CGPA of 8.3/10. Academic scholarship recipient.
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🛠 Personal & Academic Projects
Machine Learning · Deep Learning · Reinforcement Learning
PythonPyTorch
Reinforcement LearningLSTM / RNN
Supervised MLModern Portfolio Theory
📈 Stock Prediction using Reinforcement Learning and Regression
Machine Learning Project
- Applied Modern Portfolio Theory and benchmarked 3+ supervised ML models including regression, SVM, and KNN for stock price movement prediction, improving prediction accuracy by 15% over baseline.
- Implemented a Reinforcement Learning agent using PyTorch with policy gradients, epsilon-greedy exploration, and Deep Q-Network (DQN), achieving a Sharpe Ratio of 1.4 and 12% annualized return on backtested intraday trading data across 5+ equity portfolios.
🎵 Music Generation using Deep Learning
Deep Learning Project
- Trained character-level RNNs and polyphonic LSTMs with binary cross-entropy loss on a 55,000-song Kaggle dataset, achieving 85% accuracy in per-timestep piano note prediction.
- Applied truncated BPTT and gradient checkpointing to handle sequences 10x longer than standard training windows, reducing memory usage by 40% while maintaining model accuracy.
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🎓 Education & Achievements
IIT Kanpur · July 2019 – July 2023 · B.Tech Mechanical Engineering
IIT Kanpur
B.Tech Mechanical Eng
GPA: 8.3/10
Degree
Indian Institute of Technology Kanpur
Bachelor of Technology (B.Tech) in Mechanical Engineering
📍 Kanpur, India · July 2019 – July 2023
Graduated with a cumulative GPA of 8.3/10. Replaced standard mechanics courses with advanced electives in computer science and data analytics. Explored mathematical modeling, numerical optimization, and reinforcement learning.
National Achievements
Competitive Exam Rankings
JEE Advanced & Mains (2019)
- Achieved All India Rank 1522 in JEE Advanced, placing in the top 0.1% among 200,000+ shortlisted candidates.
- Secured All India Rank 1813 in JEE Mains, placing in the top 0.1% out of 1.2M+ candidates.
National Talent Search Examination (2017)
NTSE Scholar
Recipient of the prestigious NTSE Scholarship, awarded by the Government of India to the top 1,000 students nationally based on logical aptitude, mental ability, and scholastic skills.
IDFC First Bank Data Science Challenge
3rd Rank Nationwide
Secured 3rd rank out of 5,000+ participating students from IIT Kanpur in a nationwide data science case study challenge evaluating predictive performance, model explanation, and scalability.
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🛠 Technical Skills
Distributed Systems · Backend Engineering · AI & Machine Learning
JavaPythonKafka
Spring BootApache IgniteAkka
PyTorchDistributed Systems
Skill Categories
Programming Languages
Java
Python
C / C++
SQL
Frameworks & Tools
Spring Boot
Akka Actor Model
Apache Kafka
Apache Ignite
REST APIs
Microservices
Splunk
Grafana & Jenkins
MongoDB & Git
Machine Learning & AI
PyTorch
Scikit-Learn
NumPy / Pandas
Matplotlib
Neural Networks
Deep Learning / GNN
Reinforcement Learning
Concepts
Distributed Systems
Event-Driven Architecture
Algorithms & Data Structures
Agile Methodology
SDLC Processes
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📬 Get In Touch
Open to backend development, distributed systems & fintech opportunities
Available for opportunities
Hyderabad, India