Oct 2025 – Present · 7 mos · Austin, Texas, United States

Oct 2022 – Present · 3 yrs 7 mos · Austin, Texas, United States

• Leading a high-performing data science team to deliver innovative AI solutions, predictive analytics, and generative AI applications that drive business growth and enhance operational efficiency.
• 1. Spearheaded the development of AI and predictive analytics solutions (Classification/Regression models, recommendation engines, forecasting systems) that resulted in a increase in lead conversion and improvement in customer retention.
• 2. Drove the implementation of full-stack generative AI applications and AI-powered chatbots, automating key tasks and improving KPIs.
• 3. Architected and deployed Retrieval Augmented Generation (RAG) and Agentic AI systems using Cloudflare's WorkersAI, Vectorize, R2, D1, Pages, and AI Gateway, optimizing internal workflows and reducing task completion times.
• 4.Engineered MCP servers enabling seamless integration of LLMs with internal tools like Wiki and Jira, streamlining processes.
• 5. Collaborated cross-functionally with Sales, Marketing, Engineering, and Product teams, aligning data science initiatives with business objectives and contributing to a increase in overall revenue growth.
• 6. Fostered a culture of continuous learning, improving team productivity and reducing model deployment cycles through the implementation of new methodologies and tools (Docker, Kubernetes, Terraform).
• Technical Skills: ML, DL, NLP, LLMs (OpenAI, Anthropic, Gemini, LLaMA, Mistral, DeepSeek, Qwen), Python Libraries (LangChain, AutoGen, LlamaIndex, CrewAI), SQL, AWS, GCP, Cloudflare tech stack (WorkersAI, Vectorize, R2, D1, Pages, AI Gateway), TensorFlow, PyTorch, FastAPI, Terraform, Docker, Kubernetes.

Sep 2019 – Oct 2022 · 3 yrs 1 mo · Austin, Texas, United States

• Designed and deployed machine learning solutions focused on cybersecurity and customer analytics, significantly reducing security risks and enhancing business decisions.
• 1. DGA Detection: Developed transformer-based models with character embeddings, achieving a 94.3% F1 score for malware domain classification, reducing security breaches by 12%.
• 2. Phishing Detection: Built CatBoost models analyzing HTML content to detect phishing sites with 77% accuracy.
• 3. DNS Tunneling Prediction: Engineered supervised CatBoost models, identifying DNS exfiltration attempts with 94% accuracy, mitigating critical security incidents.
• 4. Customer Churn Prediction: Implemented LightGBM models to predict and reduce customer churn, enabling proactive retention strategies and decreasing churn rate by 28%.