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AI/ML Engineer with 5+ years of experience designing, developing, and deploying machine learning and generative AI solutions across cloud and enterprise environments. Skilled in predictive modeling, NLP, recommendation systems, and LLM-powered applications including RAG and AI agents. Proficient in Python and modern ML/DL stacks (PyTorch, TensorFlow, Transformers) and experienced with MLOps practices such as MLflow, Docker, Kubernetes, CI/CD, monitoring, and REST API deployment on Azure and AWS. Proven ability to optimize model performance, automate ML workflows, and deliver scalable AI solutions that improve business outcomes.…AI/ML Engineer with 5+ years of experience designing, developing, and deploying machine learning and generative AI solutions across cloud and enterprise environments. Skilled in predictive modeling, NLP, recommendation systems, and LLM-powered applications including RAG and AI agents. Proficient in Python and modern ML/DL stacks (PyTorch, TensorFlow, Transformers) and experienced with MLOps practices such as MLflow, Docker, Kubernetes, CI/CD, monitoring, and REST API deployment on Azure and AWS. Proven ability to optimize model performance, automate ML workflows, and deliver scalable AI solutions that improve business outcomes.

William Carey Daggumalli

AI Engineer, Data Scientist, AI Developer, +2

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AI/ML Engineer with 5+ years of experience designing, developing, and deploying machine learning and generative AI solutions across cloud and enterprise environments. Skilled in predictive modeling, NLP, recommendation systems, and LLM-powered applications including RAG and AI agents. Proficient in Python and modern ML/DL stacks (PyTorch, TensorFlow, Transformers) and experienced with MLOps practices such as MLflow, Docker, Kubernetes, CI/CD, monitoring, and REST API deployment on Azure and AWS. Proven ability to optimize model performance, automate ML workflows, and deliver scalable AI solutions that improve business outcomes.…AI/ML Engineer with 5+ years of experience designing, developing, and deploying machine learning and generative AI solutions across cloud and enterprise environments. Skilled in predictive modeling, NLP, recommendation systems, and LLM-powered applications including RAG and AI agents. Proficient in Python and modern ML/DL stacks (PyTorch, TensorFlow, Transformers) and experienced with MLOps practices such as MLflow, Docker, Kubernetes, CI/CD, monitoring, and REST API deployment on Azure and AWS. Proven ability to optimize model performance, automate ML workflows, and deliver scalable AI solutions that improve business outcomes.

Available to hire

AI/ML Engineer with 5+ years of experience designing, developing, and deploying machine learning and generative AI solutions across cloud and enterprise environments. Skilled in predictive modeling, NLP, recommendation systems, and LLM-powered applications including RAG and AI agents.

Proficient in Python and modern ML/DL stacks (PyTorch, TensorFlow, Transformers) and experienced with MLOps practices such as MLflow, Docker, Kubernetes, CI/CD, monitoring, and REST API deployment on Azure and AWS. Proven ability to optimize model performance, automate ML workflows, and deliver scalable AI solutions that improve business outcomes.

Skills

Experience Level

Expert

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Expert

Work Experience

AI/ML Engineer at AppFolio

February 1, 2025 - Present

Architected a Retrieval-Augmented Generation (RAG) platform for property management workflows, optimizing vector search and reducing AI response latency by 38% using LangChain, Azure OpenAI, and Pinecone. Developed and deployed LLM-powered document intelligence services (lease, invoice, maintenance) that cut manual review effort by 65% using GPT models, FastAPI, and Azure AI Studio. Fine-tuned transformer models on domain housing datasets, improving intent classification accuracy from 86% to 94% via Hugging Face Transformers and PyTorch. Built scalable inference APIs with 99.9% availability by containerizing models with Docker and deploying on Kubernetes (1M+ predictions/month). Automated ML training/validation/deployment pipelines, reducing model release cycles by 45% with MLflow, Azure DevOps, and CI/CD. Implemented monitoring and drift detection to reduce degradation incidents by 40%, and collaborated cross-functionally to integrate AI features and improve adoption by 28% using expe

AI/ML Engineer at Icertis

June 1, 2021 - December 1, 2023

Designed NLP models to extract contract clauses and legal entities from enterprise agreements, improving extraction accuracy by 31% using BERT-based models and spaCy. Implemented supervised learning models to identify contract risks and compliance issues, reducing manual legal review time by 55%. Built recommendation models for contract language suggestions based on historical negotiations, improving drafting efficiency by 35% using Scikit-learn and XGBoost. Optimized feature engineering and preprocessing for 15M+ contract records, reducing training time by 42% using Apache Spark. Integrated AI services into REST APIs for real-time inference with average response times below 250 ms using FastAPI and Azure Machine Learning. Established MLflow-based experiment tracking and model versioning for reproducibility across 100+ experiments. Partnered with data engineers to improve ETL data quality by 30% using validation rules and anomaly detection.

Data Scientist at Incedo

May 1, 2020 - June 1, 2021

Built predictive models for customer churn and product adoption, increasing prediction accuracy by 22% using Python, Scikit-learn, and XGBoost. Conducted EDA on datasets exceeding 8M records, identifying trends that improved campaign performance by 15%. Developed preprocessing and feature engineering pipelines, reducing data prep effort by 50% using Pandas, NumPy, and SQL. Produced Power BI dashboards and automated SQL workflows to shorten executive reporting cycles by 40%. Applied clustering/segmentation to customer behavior to improve targeted marketing conversion rates by 18% using unsupervised learning. Automated recurring analytical workflows, reducing manual reporting time by 20 hours/month via Python scripting and scheduled ETL jobs. Collaborated with stakeholders to translate insights into product recommendations, improving engagement and retention.