Building production AI systems end-to-end. From frontend UX to backend APIs, cloud infrastructure, ML pipelines, and LLM orchestration. Full-stack engineer who ships AI products, not just models.
What I build: RAG pipelines, LLM agents, semantic search, ML inference engines, streaming APIs, and the infrastructure to scale them.
###☁️ Cloud & DevOps (Scaling)
###📊 ML Ops & Monitoring
Applied AI Systems (2025)
- RAG pipelines with hybrid search (dense + sparse + reranking)
- Agentic workflows with LangGraph & tool use
- LLM inference optimization (quantization, speculative decoding)
- Multi-tenant AI platforms with isolation & rate limits
- LLM evaluation frameworks (RAGAS, custom metrics)
Preparing for: Senior Applied AI Engineer interviews — covering Transformer internals, RAG system design, RLHF, vector databases, and production trade-offs.
End-to-end ownership: Frontend → API → ML Pipeline → Deployment → Monitoring
Production-first: No AI-in-a-notebook. Every feature must scale, have fallbacks, and can be evaluated.
Trade-off thinking: RAG vs fine-tuning, latency vs accuracy, cost vs quality — I architect based on constraints.
System design: RAG isn't just embeddings + LLM. It's chunking strategy, hybrid retrieval, reranking, fallbacks, and evals wired together.
- Applied AI Engineer roles (GenAI, RAG, LLM systems)
- Full-stack ML/AI architecture design
- Scaling AI systems from prototype → production
- Mentoring engineers on AI system design
Email: [email protected]
LinkedIn: [Your LinkedIn]
GitHub: [Your GitHub org]
Building AI systems that actually work in production, not just in Jupyter notebooks.