Elijah Smith
Los Angeles,CA
Summary
Senior Machine Learning Engineer with 10+ years of experience designing, deploying, and scaling AI/ML systems across Healthcare, Finance, and EdTech sectors. Specialized in Generative AI, NLP, LLMs, and MLOps with deep expertise in cloud-native pipelines on Azure and AWS. Proven track record of building production-ready models, modernizing legacy systems, and accelerating time-to-insight through scalable data and ML infrastructure. Adept at leading cross-functional initiatives, aligning AI solutions with business goals, and mentoring engineering teams.
Overview
10
10
years of professional experience
Work History
Senior Machine Learning Engineer
Huma
05.2021 - Current
• Built a high-performance Retrieval-Augmented Generation (RAG) platform using LangChain and LlamaIndex, boosting healthcare data retrieval and user insights.
• Architected and deployed enterprise-grade LLM-based recommendation systems using Azure Cognitive Search (Azure AI Search) and custom embeddings, increasing user engagement by 15%.
• Designed and implemented robust data pipelines using Apache Spark on Databricks, enabling seamless data processing and accelerating analytical workflows.
• Developed and fine-tuned LLMs and NLP models (BERT, GPT) for summarization, classification, and chatbots using PyTorch and TensorFlow — improving accuracy by 20%.
• Integrated ML models into real-time claims, care coordination, and communications systems using Python and Azure ML endpoints.
• Optimized data pipelines with PySpark and Azure Data Factory, reducing ETL latency by 25% and data wrangling effort by 30%.
• Led migration from a monolithic Django system to microservices (Node.js, FastAPI, MongoDB), enhancing system scalability and fault tolerance.
• Engineered and queried large-scale structured and semi-structured datasets with Hive, Azure SQL, and Cosmos DB (Gremlin API).
Senior Machine Learning Engineer
Capital One
05.2017 - 05.2020
• Developed and productionized advanced ML solutions for fraud detection, risk mitigation, financial forecasting, and customer experience, leveraging predictive analytics, anomaly detection, NLP, and deep learning architectures (CNNs, RNNs).
• Designed and deployed ML models for fraud detection, financial forecasting, and risk scoring using CNNs, RNNs, and NLP techniques.
• Built scalable ML pipelines on AWS (S3, SageMaker, EMR, Neptune, Kendra) and Apache Spark/Airflow/Glue, reducing data processing time by 40%.
• Developed reusable feature engineering frameworks to accelerate experimentation and improve model interpretability.
• Implemented real-time anomaly detection systems, cutting false positives and reducing fraud latency.
• Modernized AI platform by refactoring React.js/TypeScript workflows, improving model deployment efficiency and frontend UX.
• Collaborated with product managers and analysts to launch customer-facing GenAI tools (recommendation engines, chatbots).
Data Scientis - Machine Learning Focus
Kaplan
11.2015 - 05.2017
• Built ML models for student performance prediction, adaptive assessments, and dropout risk using supervised learning and anomaly detection.
• Applied NLP (NER, document classification, info extraction) to personalize curriculum and learning platforms.
• Developed data pipelines and tooling in Python, SQL, and JavaScript, accelerating EDA and model training workflows.
• Created dashboards in Tableau and Power BI to report on student outcomes and engagement metrics.
• Leveraged Hadoop-based infrastructure for ingesting and analyzing large-scale education datasets.
• Collaborated with academic researchers to evaluate learning analytics strategies and translate research insights into data-driven interventions.
Education
Master of Science - Computer Science
University of Southern California
Los Angeles, CA11-2015
Skills
- Core Languages & Frameworks: Python, TypeScript, FastAPI, Flask, Nodejs, React, Django, GraphQL, PostgreSQL, MongoDB, Gherkin
- Machine Learning & AI Frameworks: PyTorch, TensorFlow, JAX, Keras, Scikit-learn, spaCy, ONNX Runtime, Hugging Face Transformers, OpenAI API, LangChain, LlamaIndex, BentoML
- Generative AI & LLMs: GPT-4, LLaMA, Claude, Mistral, PaLM, Falcon, BERT, T5 RAG, LoRA, QLoRA, RLHF, MoE, KV Caching, Distillation
- Cloud, MLOps & CI/CD: Azure (Azure ML, AI Search, Data Factory, Cosmos DB), AWS (SageMaker, Bedrock, Lambda, EMR, Kendra, Neptune), GitLab, Azure DevOps, Docker, Kubernetes, Triton Inference Server, TensorFlow Serving, Kedro, Airflow
- Data Science & Engineering: Pandas, NumPy, SciPy, Apache Spark, Databricks, Kafka, Airflow, Flink, Delta Lake, Hive, Cosmos DB, DuckDB, Hudi, SQL Server, Feature Stores
Timeline
Senior Machine Learning Engineer
Huma
05.2021 - Current
Senior Machine Learning Engineer
Capital One
05.2017 - 05.2020
Data Scientis - Machine Learning Focus
Kaplan
11.2015 - 05.2017
Master of Science - Computer Science
University of Southern California
Key Achievements
- Generative AI & LLMs: Architected and deployed enterprise-grade LLM applications using Azure OpenAI Service, LangChain, and LlamaIndex, enabling advanced conversational AI, knowledge extraction, and Q&A systems. Applied fine-tuning and parameter-efficient methods (LoRA, QLoRA) to improve model accuracy by 20% across text summarization, classification, and recommendation use cases.
- Machine Learning & NLP: Designed and productionized ML models for fraud detection, financial forecasting, and healthcare recommendations using CNNs, RNNs, BERT, and GPT-based architectures. Implemented advanced NLP techniques for sentiment analysis, NER, and document classification, improving customer engagement and operational efficiency.
- Data Engineering & ETL: Built and optimized large-scale data pipelines with PySpark, Apache Airflow, and Azure Data Factory, reducing processing times by up to 50% and ETL overhead by 30%. Engineered and managed structured and semi-structured datasets with Hive, Azure SQL, and Cosmos DB for enterprise-scale analytics and relationship modeling.
- Cloud & Big Data Platforms: Designed and deployed scalable AI/ML infrastructure on Azure (Azure ML, AI Search, Data Factory, Cosmos DB) and AWS (SageMaker, Bedrock, Lambda, Neptune). Leveraged Spark, Hadoop, and Kafka to scale analytics and ML solutions for financial, healthcare, and education domains.
- MLOps & CI/CD: Implemented CI/CD workflows for ML deployment using GitLab, AWS, and Azure DevOps. Automated model training, evaluation, and deployment pipelines with Docker, Kubernetes, and Python, accelerating time-to-production by 30%.
- System Modernization: Migrated monolithic systems to microservices using FastAPI, Node.js, and MongoDB, improving scalability, resilience, and fault tolerance across business-critical applications.
- Visualization & Insights: Developed interactive dashboards and reports in Tableau and Power BI, delivering actionable insights on fraud detection, patient outcomes, and student performance to business stakeholders and leadership.
- Leadership & Collaboration: Partnered with cross-functional teams—including product managers, engineers, and business stakeholders—to design AI-powered solutions such as recommendation engines, automated customer support systems, and personalized learning platforms, driving measurable business outcomes.