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I am a Senior Machine Learning Engineer and Data Scientist with over 9 years of experience delivering enterprise-grade AI and ML solutions across finance, healthcare, and SaaS sectors. I specialize in Generative AI, NLP, time-series forecasting, and fraud detection, with a solid track record building RAG-based chatbots, VQA systems, and full-stack ML pipelines. My expertise includes developing and deploying scalable machine learning models using TensorFlow, PyTorch, Huggingface Transformers, and Azure ML, operationalized via RESTful APIs, Docker, and Kubernetes for real-time inference. I am skilled in exploratory data analysis, A/B testing, and statistical modeling to drive data-informed decisions, with a deep focus on feature engineering, data-centric AI, and model optimization techniques such as quantization, pruning, and knowledge distillation. With strong leadership capabilities, I guide ML teams, mentor engineers, and collaborate closely with cross-functional stakeholders to align AI initiatives with business goals while promoting ethical standards in fairness and transparency.…I am a Senior Machine Learning Engineer and Data Scientist with over 9 years of experience delivering enterprise-grade AI and ML solutions across finance, healthcare, and SaaS sectors. I specialize in Generative AI, NLP, time-series forecasting, and fraud detection, with a solid track record building RAG-based chatbots, VQA systems, and full-stack ML pipelines. My expertise includes developing and deploying scalable machine learning models using TensorFlow, PyTorch, Huggingface Transformers, and Azure ML, operationalized via RESTful APIs, Docker, and Kubernetes for real-time inference. I am skilled in exploratory data analysis, A/B testing, and statistical modeling to drive data-informed decisions, with a deep focus on feature engineering, data-centric AI, and model optimization techniques such as quantization, pruning, and knowledge distillation. With strong leadership capabilities, I guide ML teams, mentor engineers, and collaborate closely with cross-functional stakeholders to align AI initiatives with business goals while promoting ethical standards in fairness and transparency.

Kevin Barnes

AI Engineer, Data Scientist, Full Stack Developer, +2

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I am a Senior Machine Learning Engineer and Data Scientist with over 9 years of experience delivering enterprise-grade AI and ML solutions across finance, healthcare, and SaaS sectors. I specialize in Generative AI, NLP, time-series forecasting, and fraud detection, with a solid track record building RAG-based chatbots, VQA systems, and full-stack ML pipelines. My expertise includes developing and deploying scalable machine learning models using TensorFlow, PyTorch, Huggingface Transformers, and Azure ML, operationalized via RESTful APIs, Docker, and Kubernetes for real-time inference. I am skilled in exploratory data analysis, A/B testing, and statistical modeling to drive data-informed decisions, with a deep focus on feature engineering, data-centric AI, and model optimization techniques such as quantization, pruning, and knowledge distillation. With strong leadership capabilities, I guide ML teams, mentor engineers, and collaborate closely with cross-functional stakeholders to align AI initiatives with business goals while promoting ethical standards in fairness and transparency.…I am a Senior Machine Learning Engineer and Data Scientist with over 9 years of experience delivering enterprise-grade AI and ML solutions across finance, healthcare, and SaaS sectors. I specialize in Generative AI, NLP, time-series forecasting, and fraud detection, with a solid track record building RAG-based chatbots, VQA systems, and full-stack ML pipelines. My expertise includes developing and deploying scalable machine learning models using TensorFlow, PyTorch, Huggingface Transformers, and Azure ML, operationalized via RESTful APIs, Docker, and Kubernetes for real-time inference. I am skilled in exploratory data analysis, A/B testing, and statistical modeling to drive data-informed decisions, with a deep focus on feature engineering, data-centric AI, and model optimization techniques such as quantization, pruning, and knowledge distillation. With strong leadership capabilities, I guide ML teams, mentor engineers, and collaborate closely with cross-functional stakeholders to align AI initiatives with business goals while promoting ethical standards in fairness and transparency.

Available to hire

I am a Senior Machine Learning Engineer and Data Scientist with over 9 years of experience delivering enterprise-grade AI and ML solutions across finance, healthcare, and SaaS sectors. I specialize in Generative AI, NLP, time-series forecasting, and fraud detection, with a solid track record building RAG-based chatbots, VQA systems, and full-stack ML pipelines. My expertise includes developing and deploying scalable machine learning models using TensorFlow, PyTorch, Huggingface Transformers, and Azure ML, operationalized via RESTful APIs, Docker, and Kubernetes for real-time inference.

I am skilled in exploratory data analysis, A/B testing, and statistical modeling to drive data-informed decisions, with a deep focus on feature engineering, data-centric AI, and model optimization techniques such as quantization, pruning, and knowledge distillation. With strong leadership capabilities, I guide ML teams, mentor engineers, and collaborate closely with cross-functional stakeholders to align AI initiatives with business goals while promoting ethical standards in fairness and transparency.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Full Stack Engineer (R&D, AI/ML) at GitHub

May 1, 2021 - Present

Led the end-to-end development of a Retrieval-Augmented Generation (RAG) chatbot leveraging LangChain, FAISS, and OpenAI APIs, reducing support response times and enhancing internal knowledge accessibility. Designed and deployed Generative AI workflows with GPT-3 and Hugging Face Transformers, automating content generation to increase marketing and documentation efficiency by 60%. Integrated large language model-driven personalization into client-facing applications, significantly improving user experience and boosting adoption. Streamlined development by embedding AI tools like GitHub Copilot, allowing greater focus on innovation and system design. Built scalable systems using agile methodologies, developed and optimized deep learning pipelines in TensorFlow applying model compression techniques, and applied data-centric AI strategies like weighted sampling and embedding visualization to improve model generalization. Delivered AI models across deep learning, reinforcement learning, an

Full Stack Engineer (ML) at Deloitte

May 1, 2021 - August 6, 2025

Pioneered data-centric AI techniques including intelligent sampling, real-time embeddings, and automated data cleansing, contributing to multiple pending patents. Directed a team of 10 ML engineers to develop an interest rate prediction engine for a $1B fintech client, driving a 90% increase in consumer spending within 12 months. Architected and deployed large-scale fraud detection systems using NLP and anomaly detection, preventing multimillion-dollar financial losses annually. Built robust NLP pipelines with BERT, Gensim, spaCy, and NLTK for analytics, compliance, and reporting automation. Designed and implemented time-series forecasting models using XGBoost and Random Forest for KPI tracking and business optimization. Developed scalable ML workflows for experimentation and deployment with TensorFlow, H2O, Docker, Kubernetes, Apache Airflow, and Apache Spark. Collaborated with senior leadership to align ML initiatives with strategic business goals and contributed to executive reporti

Software Engineer (Data & Web) at Envision Consulting, LLC

December 31, 2016 - August 6, 2025

Built machine learning models for invoice classification utilizing Python libraries such as Pandas, NumPy, SciPy, Scikit-learn, and NLTK, significantly improving processing speed and accuracy. Automated document labeling by integrating AWS CloudSearch with Python, enhancing scalable classification and reducing manual annotation efforts. Developed NLP pipelines applying tokenization, POS tagging, and classification to extract key insights from documents. Audited and cleansed Critical Data Elements with rule-based validation and anomaly detection to maintain enterprise data integrity. Supported big data ingestion by importing log data into HDFS using Flume and Sqoop, enabling downstream Hive analysis. Worked within Agile, cross-functional teams to accelerate analytics delivery and improve stakeholder engagement.