Early-career PhD-level Software Engineer at Google (London) focused on building data processing pipelines, developing engineering tools with research teams, and improving machine-learning model quality through data optimization.
AI/GenAI engineer on the AI Core team designing and deploying production ML and LLM features — reimbursable cost matching, anomaly detection, and in-month billing monitoring copilots — using Google Vertex AI, Gemini, and GCP data services within an enterprise billing pipeline.
Machine Learning Engineer developing and operationalizing production ML systems (NLP, computer vision, embeddings, LLMs, multimodal LLMs) for a mission-driven investigative platform that helps law enforcement detect child exploitation, deployed at scale on AWS.
Senior ML engineer on Nebius's AI R&D team doing applied research on LLMs and agents — running reinforcement learning, guided search, web-scale data collection, and distillation experiments at large scale using Python and JAX.
ML Infrastructure Engineer at Nebius benchmarking and profiling GPU platforms for deep learning/AI workloads, optimizing training and inference performance, and building internal tooling and dashboards. Core stack spans CUDA, ROCm, NCCL, PyTorch, JAX, Megatron-LM, TensorRT-LLM, Docker, and Kubernetes.
Senior Applied ML Engineer at Nebius building agent-native search infrastructure for AI systems, designing and deploying ML models for retrieval, reranking, and indexing at production scale. Tech spans Python/Go/C++, deep learning, embeddings, transformers, and LLM-integrated retrieval systems.