Junaid Taylor

I am a results-driven Machine Learning and AI engineer with 10 years of experience designing, deploying, and scaling advanced ML, NLP, computer vision, and generative AI solutions. I focus on turning research and prototypes into production-grade systems that deliver business impact.

My technical work includes LLM fine-tuning, retrieval-augmented generation (RAG), time series forecasting, recommendation systems, and model explainability using SHAP and LIME. I am proficient in Python, PyTorch, TensorFlow, Hugging Face, and a broad MLOps toolset including MLflow, Docker, Kubernetes, and CI/CD across AWS, GCP, and Azure.

I have led end-to-end projects integrating Apache Spark, Kafka, and Airflow for real-time pipelines, and delivered enterprise-grade AI products from semantic search with vector embeddings to interactive ML applications. My deployments emphasize monitoring, drift detection, versioning, and continuous improvement to maintain production reliability.

I enjoy mentoring engineers and collaborating with cross-functional teams to define AI product strategy, optimize costs, and build scalable architectures. I seek roles where I can architect and operationalize generative AI and MLOps solutions that drive measurable outcomes.