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Report abuseI'm a software engineer who transforms complex challenges into elegant solutions that scale. From optimizing CUDA kernels for 1.46x speedups to building real-time platforms with sub-500ms latency, I thrive at the intersection of technical excellence and business impact.
My approach is simple: measure twice, optimize once, ship constantly. Whether it's achieving 94% accuracy in production ML systems or rendering 1M+ points at 858 FPS, I believe in pushing the boundaries of what's possible while keeping the user experience at the center.
Currently seeking opportunities to tackle meaningful challenges at companies building the future.
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94% accuracy 120ms latency Production RAG Built a production RAG system with fine-tuned Llama-3.1-8B that matches GPT-4 quality at a fraction of the cost. Implemented custom attention caching that reduced latency by 73%, enabling real-time responses. Technical Deep Dive
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<500ms sync WebSocket protocol 85% bandwidth optimized Created a video watch party platform with perfect synchronization across distributed clients. Engineered a binary WebSocket protocol with delta compression, achieving sub-500ms latency for seamless real-time collaboration. Technical Deep Dive
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1.46x speedup 95.3% bandwidth utilization Kernel fusion Developed fused CUDA kernels for transformer models, achieving near-theoretical memory bandwidth utilization. This optimization enables significantly faster inference for large language models through innovative kernel fusion techniques. Technical Deep Dive
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858 FPS 1M+ points 7.2x faster Built a 3D point cloud viewer that outperforms industry standards by 7.2x. Implemented custom spatial indexing and SIMD optimizations to achieve real-time rendering of massive datasets. Technical Deep Dive
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Core Languages:
Expert: [Python, TypeScript, C++, JavaScript]
Proficient: [CUDA, SQL, Bash]
AI/ML Stack:
Frameworks: [PyTorch, Transformers, LangChain, scikit-learn]
Techniques: [Fine-tuning, RAG, Embeddings, Vector Search]
Production: [ONNX, TensorRT, Model Quantization, Batching]
Backend Engineering:
Python: [FastAPI, Django, Flask, Celery]
Node.js: [NestJS, Express, Socket.IO, Bull]
APIs: [REST, GraphQL, gRPC, WebSockets]
Frontend Development:
Core: [React, Next.js, Redux, TypeScript]
UI: [Tailwind CSS, Material-UI, Framer Motion]
Advanced: [Three.js, D3.js, WebRTC, Canvas API]
Data & Infrastructure:
Databases: [PostgreSQL, MongoDB, Redis, Elasticsearch]
Vector DBs: [Pinecone, FAISS, Chroma, Qdrant]
Message Queues: [RabbitMQ, Kafka, Redis Pub/Sub]
DevOps & Cloud:
Containers: [Docker, Docker Compose, Buildkit]
Orchestration: [Kubernetes, Helm, ArgoCD]
CI/CD: [GitHub Actions, GitLab CI, Jenkins]
Cloud: [AWS (EC2, S3, Lambda), GCP, Vercel]
Performance & Systems:
GPU: [CUDA, cuDNN, Thrust, OptiX]
CPU: [SIMD, OpenMP, Threading, Profiling]
Graphics: [OpenGL, Vulkan, Shaders]| Capability | Evidence |
|---|---|
| 🏗️ Full Product Ownership | Shipped end-to-end solutions from concept to production |
| ⚡ Performance Excellence | 1.46x-7.2x improvements across different domains |
| 📊 Production Experience | Deployed scalable systems with real-world usage |
| 🎯 Technical Precision | 94% ML accuracy, 95.3% GPU efficiency achieved |
| 🚀 Rapid Execution | From idea to MVP in days, not months |
I'm excited about joining teams that are:
I'm always excited to discuss challenging problems and explore how I can contribute to your team's success.
Whether you're building the next breakthrough in AI, scaling systems to billions, or creating products that change lives - let's talk.
Production-ready RAG system with fine-tuned Llama-3.1-8B for expert-level domain Q&A
Python
Fused normalisation CUDA kernels for PyTorch — residual-add+LayerNorm/RMSNorm, fp8 outputs, fast deterministic backward, torch.compile-native, with reproducible A100/H100 benchmarks
Python 1
Real-time video synchronization for YouTube watch parties. React + NestJS + Socket.IO, PostgreSQL/Prisma, Redis. JWT auth with token revocation, sliding sessions and Google OAuth; exactly-once sync…
TypeScript 1
Measurement-driven PyTorch training autotuner — tries precision × torch.compile × memory-format configs on your model, keeps the fastest, never returns one slower than your baseline. Honest, commit…
Python 1
Google OR-Tools constraint solver scheduling 500+ students. Flask/PostgreSQL/Redis backend, Docker/K8s deployment, CI/CD. Zero conflicts, 60-second optimization.
Python
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