AI Engineer and Machine Learning Engineer — LLM systems, generative AI, and production MLOps delivery.

I work across the full AI delivery stack: data pipelines, experimentation, model fine-tuning, inference optimization, deployment, and monitoring. My recent work spans generative AI systems, enterprise chatbots, speech-to-text pipelines, semantic search, video intelligence, and production recommendation engines.

The focus is consistent: ship systems that are reliable, cost-aware, and tied to business outcomes rather than demos. That includes LoRA/QLoRA fine-tuning of open-weight LLMs, RAG pipelines with FAISS and Pinecone, real-time inference, and ML platform practices including CI/CD, MLflow, and experiment tracking.

Deep learning work is built on PyTorch — from transformer architectures to distributed training across multi-GPU setups. Applied to fine-tuning LLaMA and Mistral models, building custom vision encoders, and optimizing models for production inference with quantization and dynamic batching.

On the infrastructure side: Docker, Kubernetes, AWS, and GCP Vertex AI for ML workload orchestration, with automated monitoring, drift detection, and retraining pipelines. Machine learning system design — from data versioning through model registry to serving — is as integral to the work as the models themselves.