Software Engineer, Programmer & Deep Learning Specialist. Designing production-grade LLM pipelines, neural network architectures, and high-concurrency microservice systems.
Bridging theoretical deep learning research with reliable, observable software engineering.
Engineering resilient RAG pipelines, multi-stage semantic rerankers, autonomous agent loops, and AI gateway routing with graceful degradation strategies.
Transformer fine-tuning (LoRA / QLoRA), embedding model adaptation, loss formulation, and inference optimization using ONNX Runtime and TensorRT.
Clean service architectures, high-performance asynchronous REST microservices, containerization, deterministic CI/CD, and comprehensive integration testing.
Representative implementations showcasing data ingestion, inference pipelines, and autonomous tooling.
Scalable ingestion architecture with recursive text chunking, Pinecone vector indexing, and a unified LiteLLM router proxy handling load balancing and fallback across LLM providers.
Optimized deep neural network inference pipeline reducing p99 latency by 45% using INT8 post-training quantization and dynamic batching inside containerized runtimes.
Orchestrated team of specialized autonomous agents auditing codebases across correctness, security vulnerabilities, and unit test coverage with synthesized verdicts.
A structured breakdown of core languages, deep learning frameworks, and infrastructure tools.
State-of-the-art Generative AI models are only as effective as the engineering infrastructure around them. Reliable production AI demands deterministic fallback pathways, clean ingestion pipelines, rigorous benchmark evaluations, and low-latency API layers.
Whether training neural networks or architecting scalable asynchronous backend services, my priority is writing modular, clean, and battle-tested code.
Available to discuss Generative AI engineering, deep learning infrastructure, or senior software engineering roles.