Agentic AI

● Architected and led implementation of a high-scale vector search over 100M+ records, achieving 2x latency reduction and 2.35% lift in core relevance metrics; served as foundational infrastructure for enabling Retrieval-Augmented Generation (RAG) systems.

● Participated in company-wide architecture decision records meetings.

● Conducted offline evaluation of search relevancy using OpenAI’s batch API and AWS Bedrock LLMs

● Built GPU-accelerated vectorization pipelines in Databricks, efficiently processing 100M+ records for large-scale ML workloads.

● Performed hardware cost evaluation and optimization for OpenSearch reducing infrastructure expenses by $240,000 annually.

● Benchmarked hybrid search, revealing 38% CPU usage at 100 RPM with only 4% NDCG gain; recommended deprioritization based on cost-benefit analysis.

● Created scalable applications using Python, Spring Boot, Docker, deployed on AWS ECS for seamless scalability and performance.

● Analyzed A/B testing results to uncover user behavior patterns and inform relevance tuning.

● Influenced a company-wide shift in understanding of user search behavior by formalizing match types (exact, beneficial, bad). Which caused enabling a classifier that achieved ~2x F-score improvement over the baseline and reduced LLM usage, leading to operational cost savings.

● Proposed external applications integration (Discord, Telegram, WhatsApp bots) with internal APIs for enhanced user engagement.

● Authored and maintained Terraform templates supporting infrastructure as code for OpenSearch, EC2, and ECS.

● Coordinated with AWS and Hugging Face SMEs to leverage advanced ML tools and ensure industry best practices.

● Mentored engineers across teams on RAG-related projects, supporting cross-functional collaboration.

● Mentored interns on the Search Engineering Team, fostering technical growth and contribution.

● Automated 500+ test cases in Robot Framework + Python environment

● Performed offline comparison of search engines using Pandas, Numpy, scikit learn, fasttext libraries in Jupyter Notebook (3000+ lines of code)

● Designed REST API automation for SOLR and microservices’ endpoints

● Deployed Data Science microservices in AWS ECS

● Performed offline testing analysis for search engine precision optimisation using DS model.

● Extracted data from Redshift database and parsed it with pandas library

● Enhanced testing framework with APIs for MongoDB interactions

● Created application that returns prediction of similar topics based on input string using (React JS) and BE (using Python’s Falcon and gensim). BE was hosted using Docker container

● Integrated support of Chrome Extension automation for testing framework using image recognition library

● Added automation in jenkins pipelines for production deployment verification

● Created load tests using Gatling for simulating production traffic in test environment

● Established dockerfiles for building images with enhanced functionality

● Performed deployment to production environments with following assertion of post deployment stability using Logz.io and New Relic applications

● Set up alerts in New Relic for production environment

● Participated in team that took second place in Hackathon

● Took part in multi continental meetings (Israel, India, Ukraine) with Project Owners, Developers, UX designers, providing suggestions for stories implementation

● Performed PR reviews of QA engineers and developers in GIT

● Created documentation to formalize release process in subteam

● Created documentation for production environment setup

● Interviewed candidates for QA Engineer position

● Tested PS4 framework applications and web-based applications

● Automated test cases using Python and Node JS.

● Used Docker, MySQL and Shell scripts to simulate production environment based on Ember.js

● Participated in meetings with Project Owners, Developers providing suggestions for stories implementation

● Performed PR reviews of QA engineers in GIT

● Used REST API for data population

● Performed on-boarding trainings for software engineers. Created video walkthroughs for most prevailing features

● Filed bug reports. Negotiated bugs with beta testers

● Documented automation software setup

• Tested web-based cloud security application that provided security Office 365, Google Apps, SalesForce, Exchange,

• REST API, Soap API testing with Python

• Experience in Performance test automation;

• Programming in Google script

• Uploaded automated test cases to Jenkins using GIT repository

• Automated test runs in Robot Framework using Data-driven and Behavior-driven styles

• Managed Jenkins

• Mobile Test Automation with Appium

• Tested on mobile devices including Samsung Galaxy tab 4, iPad mini, iPod, Windows RT

• Created test plan, test cases, test runs for stories assigned to me at Bugzilla bug tracking system

• Added command in Task Manager on Windows to run Ccleaner automatically when load on machine that was using Jenkins was minimal

• Used Secured Shell to access log data

• Used HMA! pro VPN application to simulate signing in to the developed product from different locations

• Performed testing from remote virtual machines

• Used AD FS 2.0 to create users and verified population of user detail settings via SAML (attributes

• Reported bugs and included log data in them

• Negotiated with PM enhancements for various types of stories

• Provided QA intern training