I am a Data & Applied Scientist with 3+ years of experience building end-to-end machine learning systems, scalable data pipelines, and production-grade Generative AI applications. My expertise lies at the intersection of data science, applied ML research, large-scale engineering, and AI infrastructure, enabling enterprise teams to operationalize ML and LLMs reliably and efficiently. I have worked extensively with LLMs, multimodal models, retrieval systems, and distributed ML platforms, creating impactful solutions that improve developer productivity, automate complex workflows, and drive measurable business outcomes. My work spans the full lifecycle—from problem formulation and statistical experimentation to deployment, optimization, and monitoring in production. At Juniper Networks and Arista Networks, I built and deployed large-scale AI systems used across global engineering and customer-facing teams: • Fine-tuned LLaMA and Qwen using LoRA/QLoRA → improved response quality by 23%, reduced latency by 18% • Built multilingual RAG and semantic retrieval pipelines → increased accuracy from 42% → 75% • Designed Transformer-based classifiers & recommendation algorithms → improved customer satisfaction by 30% • Developed LLM evaluation frameworks (MT-Bench, TruthfulQA, MMLU) → improved scoring consistency 4.3× • Engineered MLOps pipelines (Kubeflow, MLflow, K8s, Docker) → reduced retraining latency by 90%, enabled daily pipelines • Built Responsible AI guardrails for safety, grounding, bias, and hallucination detection → reduced hallucinations by 40% • Created internal AI assistants, SDKs, APIs, and developer tooling → accelerated experimentation and onboarding by 40% As a Data & Applied Scientist, I combine strong analytical capabilities—statistics, experimentation design, causal methods—with deep hands-on engineering experience across distributed systems, feature engineering, and scalable model deployment. I’ve worked with large datasets (SQL, PySpark, distributed compute), architected data pipelines, and delivered actionable insights that directly shaped product and AI strategy. I’m passionate about building reliable, secure, high-impact AI systems, evaluating and integrating open-source LLMs, and developing internal tools that make AI accessible across organizations. I thrive in environments that encourage experimentation, engineering rigor, and collaboration between scientists, developers, and product teams. If you’re working on Applied ML, AI infrastructure, LLM platforms, or GAI-driven productivity tools, I’d love to connect.