---
title: "Saksham Goyal - Computer Scientist in Bengaluru | Tal"
description: "Meet Saksham Goyal, Senior Software Engineer @ Adobe | Distributed Systems, Java, Spring Boot, Python, AWS | Ex-Morgan Stanley, OLX based in Bengaluru. AI stack includes antigravity, codex, and opencode."
image: "https://tal.club/meet/saksham-goyal-1/share-image"
---

# Saksham Goyal on Tal

## Headline

Senior Software Engineer @ Adobe \| Distributed Systems, Java, Spring Boot, Python, AWS \| Ex\-Morgan Stanley, OLX

## About

Senior backend engineer with 6 years of experience building distributed systems and agentic AI applications across Adobe, Morgan Stanley, and OLX\. Experience spans enterprise SaaS, financial services, and high\-traffic consumer marketplaces\. Currently a Computer Scientist \(SDE 3\) at Adobe, architecting distributed systems for the Adobe Unified Platform and building agentic AI tooling on the Model Context Protocol\. I work in Java and Python, with a focus on scalability, reliability, and observability for high\-throughput applications\. ► At Adobe: Developing the Content Logs and Storage Analytics platform, a distributed system on Java, Spring Boot, Kafka, and DynamoDB processing \~450K events/min at sub\-10ms p95 across 5 global regions\. Delivered a zero\-downtime Cassandra\-to\-DynamoDB migration that materially reduced storage spend \(\~45% lower run\-rate\)\. Built Catalyst, an MCP\-based AI productivity platform to automate recurring developer workflows\. ► At OLX: Drove a 204% lift in page visits and lead generation through three ranking strategies on the search backend serving 5M\+ daily users across 30\+ markets\. Independently launched the search platform in two new countries \(Argentina and Colombia\)\. ► At Morgan Stanley: Replaced a legacy 24\-hour batch refresh with a Kafka\-driven real\-time indexing pipeline, cutting search latency from 24 hours to under 1 second for financial advisors\. Augmented keyword search with NLP\-driven semantic capabilities, improving document search accuracy by 36%\. Built a Python recommendation framework on neural collaborative filtering and FastText embeddings achieving 81% accuracy\. Core stack: Java, Python, Spring Boot, AWS \(DynamoDB, S3, SQS, RDS\), Apache Kafka, Elasticsearch, Apache Solr, PostgreSQL, MongoDB, Redis, Kubernetes, Docker, Terraform\. Strong on system design, microservices, multithreading, and observability \(Prometheus, Grafana, Splunk\)\. I'm increasingly bridging traditional backend infrastructure with the new AI stack using agentic systems, MCP, typed tool integrations, and evaluation\-driven development\. Published research: 1\. Knowledge\-graph\-based ML enrichment \(IEEE 2020\) 2\. Multi\-task learning for financial forecasting \(Springer 2020\)\.

## Location

Bengaluru

## AI stack

antigravity

codex

opencode

## Work experience

Computer Scientist at Adobe, Jun 2025 \- Present · 1 yr 3 mos, Bangalore, \- Working on the next phase of Content Logs and Storage Analytics platform by extending it into the audit and compliance backbone that enterprise customers rely on for governance, regulatory reporting, and security forensics across Adobe Products\. Day\-to\-day this means widening event coverage, hardening fidelity, and building out the reporting layer\. \- Built Catalyst, an AI\-native productivity platform on Model Context Protocol \(MCP\) and 20\+ typed tools integrating Jira, GitHub, and other developer systems to automate recurring workflows through a single Slack interface, reclaiming 30\+ minutes daily per engineer\. Tech: Java, Spring Boot, Python, AWS \(DynamoDB, S3, SQS, IAM, CloudWatch\), Apache Kafka, Model Context Protocol \(MCP\), Kubernetes, Docker, Terraform, Prometheus, Grafana\.

Member of Technical Staff 2 at Adobe, Jan 2024 \- Jun 2025 · 1 yr 6 mos, Bengaluru, Focused on building and shipping the distributed systems behind Content Logs and Storage Analytics for Adobe Platform\. \- Developed Content Logs, a 5\-microservice distributed system on Java, Spring Boot, Kafka, and DynamoDB processing \~450K events/min at sub\-10ms p95 latency across 5 global regions\. \- Led a zero\-downtime migration of the user logs database from self\-managed Apache Cassandra to AWS DynamoDB\. Validated data fidelity end\-to\-end and materially reduced storage operational cost \(\~45% lower run\-rate\)\. Tech: Java, Spring Boot, Python, AWS \(DynamoDB, S3, SQS, RDS, CloudWatch\), Apache Cassandra, Apache Kafka, Kubernetes, Docker, Terraform, Jenkins, ArgoCD\.

SDE at OLX Autos, Oct 2022 \- Jan 2024 · 1 yr 4 mos, India, Backend engineer on OLX's search and discovery platform, the system behind search, typeahead, filters, and faceted refinement across web and mobile, serving 5M\+ daily search users across 30\+ international markets\. \- Drove a 204% lift in page visits and lead generation for aged and negative\-margin inventory by designing and shipping three ranking strategies on the search backend\. \- Owned and scaled core search and discovery features end\-to\-end, spanning the full query path from typeahead and filters through faceted refinement and result ranking\. \- Independently led the launch of the search and discovery backend in two new countries \(Argentina and Colombia\), adapting the platform to regional inventory characteristics and onboarding new customer bases\. Tech: Java, Spring Boot, Elasticsearch, Apache Solr, REST APIs, microservices, AWS, Kafka, AWS S3 etc

Senior Associate at Morgan Stanley, Jan 2022 \- Oct 2022 · 10 mos, Bangalore Urban, Backend engineer on the Wealth Management technology platform, building search, retrieval, and recommendation systems used by financial advisors across the firm\. \- Replaced a legacy 24\-hour batch refresh with a Kafka\-driven real\-time indexing pipeline for client and account search, cutting content access latency from 24 hours to under 1 second for financial advisors\. \- Augmented the legacy keyword search with NLP\-driven semantic capabilities, lifting internal document search accuracy for financial advisors by 36%\. \- Built a Python recommendation framework using neural collaborative filtering, re\-ranking, and self\-trained FastText embeddings, achieving 81% accuracy on the internal document recommendation benchmark\. Tech: Java, Python, Apache Kafka, Elasticsearch, Apache Solr, NLP, neural collaborative filtering, FastText, REST APIs, microservices\.

Technology Associate at Morgan Stanley, Jan 2021 \- Jan 2022 · 1 yr 1 mo, Bangalore Urban, Karnataka, India

Technology Analyst at Morgan Stanley, Jul 2020 \- Jan 2021 · 7 mos, Bengaluru, Karnataka, India

Research Mentor at Microsoft Innovation Lab, May 2019 \- Aug 2019 · 4 mos, Bangalore, Mentored a research project which focused on developing autonomous \(self\-driving\) cars using reinforcement learning \( on simulator \)\. This includes application of machine learning algorithms for getting better results\.

Research Intern at Microsoft Innovation Lab, Jun 2018 \- May 2019 · 1 yr, Bangalore, Developed an artificially intelligent bot capable of playing first person shooting game using reinforcement learning\. The bot played the game, Doom, with accuracy better than humans\.

Outreach and Operations Executive at SPIRIT Inc\., Feb 2018 \- Apr 2018 · 3 mos, Bangalore, Worked in "PESU I/O", a peer\-to\-peer learning platform\. The team successfully ran 40\-50 courses every semester with an average of around 30 students in each course\. My job here was to ensure smooth execution of the operations and manage corporate relations of the courses\.

## Education

Bachelor of Technology \- BTech, Computer Science, PES University

Computer Science, army public school

[View the original profile](https://tal.club/meet/saksham-goyal-1)

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