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Vetted Data Scientists in the Atlanta Metro
Pre-screened and vetted in the Atlanta Metro.
Xingyu Chen
Junior Machine Learning Engineer specializing in generative AI and computer vision
“Built production AI features for image editing and object removal, including an agent that guides users to the right pipeline, validates inputs, refines prompts, and routes requests to GPU-backed generation services. Brings hands-on experience across multimodal control, generative model optimization, and post-launch iteration driven by failure analysis and user feedback.”
Senior AI Platform Engineer specializing in agentic AI and RAG systems
Senior Data Scientist specializing in machine learning and big data analytics
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and RAG
Intern Software Engineer specializing in cloud, backend APIs, and LLM evaluation
Staff Data Scientist & GenAI/ML Engineer specializing in agentic systems and GraphRAG
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and fraud/risk modeling
Husayn El Sharif
Senior Data Scientist specializing in geospatial ML and environmental analytics
“Applied ML practitioner who deployed a near-real-time water-quality monitoring tool for Gwinnett County by fusing ESA satellite imagery with in-situ measurements to predict chlorophyll-A and support early warnings for harmful algal blooms. Also working on a multimodal deep-learning project combining skin lesion images with patient tabular/text data (TensorFlow, embeddings) to predict melanoma risk.”
Mid-level Data Scientist specializing in LLMs, fraud detection, and healthcare analytics
Mid-level Data Scientist specializing in pricing, revenue optimization, and operational efficiency
Bhargavi Karuku
Mid-level AI Engineer specializing in ML, NLP, and Generative AI
“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”