Benjamin Shvartsman
AI software engineer shipping production LLM systems end-to-end. At Chess.com, building evals and core capabilities behind AI coaching; previously delivered ML systems for recommendations and demand forecasting at OpticsPlanet.com.
Focused on operationalizing open-source models, agentic coding workflows, and token-efficient memory. Seeking roles building reliable agentic products with latency/cost/quality tradeoffs.
Cornell CS + AI, May 2026.
LinkedIn
GitHub
Experience
AI Software Engineer
Chess.com · Internship
Nov 2025 – Present
Machine Learning Engineer
Ecentria · Internship
Sep 2023 – Sep 2025 · 2 yr
- Improved personalization for OpticsPlanet.com at scale by clustering users and products using matrix factorization embeddings, powering better retrieval and recommendation relevance for millions of users.
- Developed observability tooling for recommendation systems, enabling faster diagnosis of rec quality and prioritization of experiments, by defining KPIs, instrumenting pipelines, and delivering decision-ready dashboards.
- Enhanced demand forecasting across retail and government procurement by adapting time-series modeling for intermittent demand and operationalizing results into repeatable workflows.
Writings
thoughts on technology, engineering, and problem-solving
Case Studies & Projects
real-world problem → technical solution → business value presentation
ctx: Continual Learning for Agents, Token-Efficient Skill Synthesis
A personal knowledge layer for engineers and their agents. Captures Cursor and Claude Code sessions automatically, synthesizes external sources into reusable skills, and compiles grounded context bundles at task time. This way every agent session starts from what you already know & seen.
Precision Health with ElevenLabs
A precision health platform combining an operational clinician dashboard, a real-time clinical decision support system, and a patient-facing application with a voice AI agent for seamless, real-world data collection, all deeply integrated into foundry's ontology and aip platform
AI-Powered Government Supplier Procurement
AI-Powered Government Supplier Procurement For Solving Mission Variability & Equipment Complexity
Hospital Post-Op Coordination Dashboard
A real-time hospital dashboard system that optimizes post-operative patient care through AI-driven queue prioritization, automated equipment tracking, and seamless nurse-doctor coordination. Built with Palantir Foundry ontology integration and OpenAI-powered smart workflows.
Medical Conversation Optimizer Engine
Guard‑railed chat contextualised to their health data with perplexity-like prompt suggestions for guided continuation.
Medical Agentic RAG
A medical chatbot powered by CrewAI that combines real-time RAG with interactive document analysis. The system uses specialized agents to retrieve information from trusted medical sources, Reddit experiences, and uploaded medical documents, while providing NotebookLM-style document summaries and topic-specific discussions.
Parking Payment Automation
Developed a real-time automated parking payment system to reduce costly fines for university sports facilities with estimated throughput of $3,500/week in transaction processing via SMS-triggered workflows, integrating automated scripting and multiprocessing for immediate payment execution.
Automation of Bulk USPTO Trademark Risk Assessment and Domain Availability
A comprehensive trademark conflict detection and domain availability checking system that combines USPTO trademark data with AI-powered risk assessment and domain availability verification.
Whoop Cornell Wrestling Team Aggregated Insights
Created data pipeline for visualizing and analyzing the captains' Whoop longitudinal wearable data to uncover insights into Cornell Wreslting team's recovery, performance, and sentiment.
Certifications & Coursework
continuous learning and skill development
Cornell University Coursework
Natural Language Processing, Computer Systems and Organization, OOP & Data Structures, Linear Algebra, Mathematical Foundations of Computing, Probability Models and Inference, Introduction to Machine Learning, Analysis of Algorithms, AI for Healthcare Systems, Operating Systems, Human-AI Interaction Design
Reliable Google Cloud Infrastructure: Design and Process by Google Cloud Skills Boost
Course on designing and implementing reliable, scalable cloud infrastructure on Google Cloud Platform using best practices for system architecture, fault tolerance, and operational processes.
Google Cloud Computing Fundamentals: Cloud Computing Fundamentals by Google Cloud Skills Boost
Course covering essential cloud computing concepts, Google Cloud Platform services, and foundational skills for deploying and managing applications in the cloud.
DSPy: Build and Optimize Agentic Apps by Deeplearning.AI
Comprehensive course on building, debugging, and optimizing modular GenAI agentic applications using DSPy's module-based programming model with MLflow integration and automated prompt tuning.
Safe and Reliable AI via Guardrails by Deeplearning.AI
Course on implementing AI guardrails to mitigate LLM failure modes like hallucinations and data leaks by adding input/output validation controls to applications like RAG-powered chatbots.
Practical Multi AI Agents and Advanced Use Cases with crewAI by Deeplearning.AI
Course on building collaborative multi-agent systems with external tool integration, performance evaluation, and human feedback optimization for automating complex business tasks like project planning and content creation.
Multi AI Agent Systems with crewAI by Deeplearning.AI
Course on designing collaborative AI agent teams using crewAI to automate complex multi-step business processes by assigning specialized roles and breaking down tasks across multiple customized agents.
Building Agentic RAG with LlamaIndex by Deeplearning.AI
Course on building and designing various AI agents including document reasoning agents, router agents for Q&A and summarization, and research agents with multi-document handling capabilities and debugging controls.
Building AI Voice Agents for Production
Course on building and deploying production-ready, low-latency AI voice agents using STT/LLMs/TTS plus WebRTC (via LiveKit), with turn-taking/interrupt handling, latency measurement and optimization, voice customization, and cloud scaling for many concurrent users.