Henry Hecht

Full-stack software engineer specializing in backend systems, AI infrastructure, and cloud-native applications. Experienced designing production software using Python, TypeScript, Node.js, FastAPI, and modern cloud platforms. Built scalable AI-powered developer tools, automated trading systems, and LLM applications spanning real-time data pipelines, REST/WebSocket APIs, and machine learning infrastructure.

Passionate about building high-performance software that combines distributed systems, AI, and data-driven decision making.

Quantitative Trader - Kalshi - Remote

(2026-02)

• Grew a personal prediction-market trading account from $1K to $80K over approximately four months by designing and deploying automated trading infrastructure built around real-time event forecasting.
• Designed and maintained a production software platform composed of distributed Python services, event-driven pipelines, streaming inference, and automated execution infrastructure processing real-time market data.
• Built a real-time trading platform processing hundreds of live speech segments per event with automated inference and order execution.
• Built a backend capable of monitoring 50+ concurrent markets while executing trades through REST/WebSocket APIs.
• Developed forecasting models using fine-tuned language models, survival analysis, and probability calibration to estimate event outcomes from speaker-specific transcripts.
• Engineered risk and analytics infrastructure with fractional Kelly position sizing, cross-market correlation modeling, per-strategy PnL attribution, and backtesting framework accounting for fees, slippage, and resolution edge cases.

Software Engineer - Aspen Dental - Chicago, Ill.

(2025-08 - 2026-02)

• Developed internal web interfaces for configuring, monitoring, and debugging generated MCP servers.
• Built TypeScript-based frontend tools enabling engineers to inspect generated APIs and integration status.
• Integrated frontend applications with FastAPI/Node.js backend services through REST APIs.
• Collaborated with product managers and engineering teams during sprint planning to prioritize AI platform initiatives.
• Participated in peer code reviews and architectural discussions to improve maintainability and reliability.
• Worked cross-functionally with platform and infrastructure engineers to deploy secure healthcare-compliant AI services.
• Built an automated pipeline that parsed 1,000+ internal APIs and generated production MCP servers, reducing new integration effort by approximately 90%, saving 20–30 engineering hours per integration.
• Implemented DSPy-based optimization pipeline for tool descriptions, improving tool-selection accuracy from 60% to 95%.
• Implemented healthcare-compliant architecture with Model Armor, Grafana, PII redaction, and audit trails.

Contributor - Outlierai - Remote

(2024-02 - 2025-08)

AI training data

• Produced 500+ RLHF training pairs while maintaining 95%+ reviewer acceptance
• Analyzed Python AST and control flow graphs from large open-source Python codebases including NumPy, Django, FastAPI to generate adversarial code comprehension queries testing LLM reasoning on decorators, metaclasses, and async patterns.
• Developed weighted scoring rubrics with quantitative metrics for code correctness, edge case handling, and algorithmic complexity, enabling automated quality assessment of AI-generated Python solutions.

Integration Engineer - Datamotion Inc - Morristown, NJ

(2023-08 - 2024-04)

• Designed and implemented a retrieval-augmented generation (RAG) platform using Ray, LangChain, Mistral, pgvector, and PostgreSQL to power context-aware search across Google Workspace emails and documents.
• Built scalable ingestion pipelines for enterprise content, including document parsing, embedding generation, metadata extraction, and vector indexing.
• Developed Python backend services and REST APIs supporting semantic retrieval and AI-powered document workflows for internal applications.
• Optimized retrieval quality through vector search tuning, prompt engineering, and embedding evaluation while collaborating with cross-functional engineering teams to deploy production AI features.

B.S. - Computer Science - Rutgers University