- Student at the University of Chicago pursuing a BS in Computer Science and a BA in Physics (expected 2028).
- Focused on building scalable, production-grade software and deployable AI/ML systems, with a strong foundation in systems, math, and engineering ownership.
- Actively seeking Software Engineering or AI/ML Engineering internship roles for Summer 2026.
These projects showcase demonstrated technical ownership, architecture, and real-world impact.
- AI-Driven Research Matchmaking Platform: A scalable, serverless application that intelligently matches students to research labs using semantic search and LLM-based compatibility scoring to replace the cold-email process.
- Production Architecture (GCP): Architected the entire solution on Google Cloud Platform (GCP) using Cloud Run and Cloud Load Balancing, orchestrated with Terraform (IaC) for reproducible deployment.
- Technical Stack: Built a modern full-stack application with Next.js 15 (React 19) and a high-concurrency FastAPI backend, utilizing ChromaDB for vector storage and Firestore for data persistence.
- Impact: Currently in pilot at the University of Chicago; designed for scaling to address academic communication and efficiency across higher education.
- Core System: Developed a modular Mixture-of-Experts (MoE) architecture for autonomous driving in the CARLA simulator, utilizing specialized expert networks and a gating network for decision-making. Built with PyTorch (DDP), CUDA, and Linux.
- Data Contribution: The project's pipeline resulted in two large-scale, public datasets for the autonomous driving research community:
- CARLA Autopilot Multimodal Dataset (~365 GB, 82k frames): Synchronized RGB, semantic segmentation, LiDAR, 2D boxes, and ego-vehicle states.
- CARLA Autopilot Images Dataset (~188 GB, 68k frames): Multi-camera images, control signals, and kinematics.
- Status & Learnings: Currently paused. The process provided deep expertise in high-performance data pipelines, distributed training, and the challenges of deploying AI systems.
- Ownership & Impact: Drove reliability and scalability improvements by diagnosing and fixing critical backend failures and executing major schema refactoring with zero downtime.
- Scalable Systems Design: Designed and deployed a queue-driven execution processing system to decouple heavy telemetry operations from the API, significantly reducing request latency and enabling horizontal scaling.
- Full-Stack Development: Developed full-stack admin analytics dashboards (NestJS, Next.js, Recharts) with SQL time-bucket aggregation, providing actionable insights into user growth and execution volume.
- Reliable Data Flow: Implemented a fault-tolerant, SQS-based background worker for telemetry aggregation and HubSpot CRM synchronization, ensuring reliable data delivery for downstream analytics and sales pipelines.
- Metrics & Observability: Introduced circuit execution metrics (complexity, duration) and a KPI dashboard for UTM-based marketing attribution, directly supporting growth strategy.
- Qwen vLLM on GKE: Cloud-native deployment pipeline for serving Qwen models on GKE Autopilot, provisioning NVIDIA T4 GPUs and deploying vLLM for a high-throughput, scalable inference endpoint.
- LocalRAG: Terminal LLM chat with infinite memory via FAISS-powered local vector search, enabling persistent, context-aware conversations without external servers.
- Semantic Image Search: Full-stack text-to-image retrieval: FastAPI backend, CLIP embeddings, and Next.js/Tailwind frontend.
- GovHub: A civic software concept offering a GitHub-style workflow for legislation. Built with React, Next.js, and TypeScript.
- Portfolio (ipeter.dev): This site, featuring ImmanuelAI—an LLM assistant (represented by
biography.js) designed to interactively answer technical questions for recruiters. - AI Commit: A Bash utility using the OpenAI API to automatically generate meaningful commit messages from staged diffs, improving engineering workflow quality.
| Category | Skills |
|---|---|
| Languages | Python, C++, Go, JavaScript/TypeScript, SQL |
| ML/AI | PyTorch, JAX/Flax, NumPy, Pandas, FAISS, OpenAI/Anthropic APIs, Hugging Face |
| Systems/Infra | Linux, Docker, Kubernetes, Git/GitHub, GitHub Actions, AWS, GCP, Terraform |
| Frameworks/Web | React, Next.js, Node.js, FastAPI |
| Databases | PostgreSQL, MySQL, MongoDB |
- Digital Portfolio: ipeter.dev (Includes resume and ImmanuelAI chat)
- Hugging Face Datasets: huggingface.co/immanuelpeter
