Ml Engineer Machine Learning

Joseph Lord Senior AI/ML Engineer

**********@*****.*** Linkedin +1-845-***-**** Austin, TX 78751 Summary

AI/ML Engineer with a decade of experience designing and deploying intelligent systems across finance, telecom, healthcare, and education. Specialized in decision-focused AI solutions, including RAG pipelines, LLM integration, and multi-agent systems. Skilled in building scalable architectures from ingestion pipelines to cloud deployment

(AWS, GCP, Azure), with proven success in reducing research time, improving compliance, and accelerating engineering workflows. Adept at aligning cutting-edge AI models with business needs, ensuring security, accuracy, and real-world impact.

Skills

Machine Learning • Generative AI • Deep Learning • Natural Language Processing (NLP) • Computer Vision • Large Language Models (LLMs) • Python • Java • TypeScript • PyTorch • TensorFlow • Keras • Scikit-learn • Hugging Face

• LangChain • FastAPI • Flask • Pandas • NumPy • spaCy • Redis • PostgreSQL • MySQL • MongoDB • Elasticsearch

• FAISS • Docker • Kubernetes • Airflow • Spark • gRPC • Git • CI/CD• AWS (Lambda, S3, ECS) • GCP • Microsoft Azure • Prometheus • Grafana.

Experience

Senior AI/ML Engineer Contextual AI ( Feb 2024 – Present ) Nexus – Telecom RAG Assistant for Qualcomm

Built a retrieval-augmented generation (RAG) system to help Qualcomm engineers quickly find answers in 3GPP standards, patents, and internal documents.

• Ingested and indexed millions of documents using FAISS-GPU, paired with BM25 in OpenSearch.

• Fine-tuned large language models on telecom terminology with Hugging Face Transformers and evaluated accuracy with domain-specific benchmarks.

• Deployed models on Kubernetes with NVIDIA Triton/TensorRT for fast, low-latency inference.

• Monitored performance and hallucination rates with Prometheus and Grafana dashboards and real-time logging.

• Integrated the assistant into Qualcomm’s engineering portal, returning sources with every answer.

• Reduced research time from hours to seconds with reliable, citation-based results. FinScope – Financial Knowledge Assistant for HSBC

Developed an AI assistant that retrieves and summarizes financial research, market news, and policy docs for analysts and compliance teams.

• Built ingestion pipelines for market research, financial news, and compliance manuals using PySpark and Databricks, enhancing data processing efficiency and accuracy.

• Indexed all financial documents in Pinecone’s private cloud, added filters like date and author, and used both keyword search and AI embeddings together to make results more accurate and trustworthy.

• Fine-tuned financial domain language models with Hugging Face and MLflow, improving the safety and professionalism of automated communications.

• Added compliance guardrails including audit logging, role-based access control, and output filters, ensuring adherence to regulatory standards.

• Deployed the assistant on HSBC's secure cloud with Kubernetes and GPU inference, ensuring enterprise-grade privacy and operational efficiency.

• Integrated the solution into MS Teams chatbot and internal portals, enhancing user engagement and system usability through iterative improvements from user feedback loops.

• Cut research and policy search time by 60%, improving compliance accuracy and analyst productivity. Senior Machine Learning Engineer Western Governors University ( Apr 2022 – Feb 2024) CoachDesk - Multi-Agent AI System Prototype

• Set up AI agents for different roles (tutor, grader, coach) using GPT-4 APIs, LangChain, and Python, enhancing the system's ability to automate educational tasks.

• Created mock student data and built small databases with pandas and SQLite to test and improve AI's ability to personalize responses, leading to more tailored educational interactions.

• Wrote prompts and tested conversation flows in Jupyter notebooks to ensure seamless collaboration among agents, improving the overall user experience.

• Built simple connections between agents with FastAPI and Redis, enabling indirect communication and enhancing system integration.

• Packaged prototypes with Docker and used GitHub Actions and AWS Lambda to share demo versions, facilitating easier deployment and testing.

• Tracked performance with Prometheus and Grafana, logging results to identify and address AI performance issues, leading to improved system reliability.

Machine Learning Engineer Kasisto ( Sep 2020 – Mar 2022 ) AI-Powered Hackathon Assistant

• built retrieval-augmented chatbot using embeddings and GPT-3 to provide real-time suggestions for project descriptions, titles, and tags.

• Implemented FastAPI to connect the chatbot with a retrieval system for fetching hackathonspecific FAQs and guidelines, enhancing user query relevance.

• Integrated sentence embeddings for similarity searches to retrieve the most relevant content, ensuring responses were specific and contextually accurate.

• Leveraged Hugging Face Transformers to fine-tune LLMs for generating hackathon announcements, FAQs, and project summaries.

• Used MongoDB to store user profiles, chatbot interaction logs, and submission data, enabling real-time retrieval and personalized assistance.

• Deployed chatbot and RAG-powered systems using AWS Lambda for serverless execution, enhancing system scalability and reducing operational costs.

• Used Docker to containerize applications and managed deployments on AWS ECS, ensuring reliable service for thousands of concurrent users.

• Monitored RAG system performance with Elasticsearch, tracking latency, retrieval accuracy, and response coherence to maintain high-quality user interactions.

• Optimized MongoDB queries and indexing to reduce latency, resulting in faster and more efficient chatbot interactions.

• Collaborated using Git for version control and team coordination, improving code quality and project efficiency. Machine Learning Engineer NarrativeDx ( July 2018 – Aug 2020 )

• Designed and deployed AI-driven chatbots using Microsoft Bot Framework to automate customer interactions and support workflows.

• Built NLP pipelines for intent detection, entity recognition, and sentiment analysis using spaCy, NLTK.

• Enhanced language understanding capabilities with word embeddings (Word2Vec) and pre-trained transformer model BERT, adapting them for domain-specific tasks.

• Added multilingual support using Google Translate API and custom pipelines for non-English text processing.

• Built RESTful APIs using Django to handle requests from chatbots and manage communication with backend services.

• Integrated Django with PostgreSQL using SQLAlchemy, improving data retrieval and storage efficiency for faster application performance.

• Deployed the Django backend on Google Kubernetes Engine (GKE) instances, enhancing scalability and reliability of containerized applications.

• Integrated with Amazon S3 for file storage, ensuring secure and efficient handling of multimedia content during chatbot interactions.

• Implemented original Transformer models using PyTorch, enhancing the accuracy and efficiency of machine learning tasks.

Computer Vision Engineer BairesDev ( Jan 2015 – Jun 2018 )

• Designed machine learning models for disease prediction and medical diagnostics using Pythscikit-learn, and early versions of TensorFlow and Keras, improving diagnostic accuracy in pilot testson.

• Built custom CNNs for medical image classification and enhanced performance with transfer learning using AlexNet and VGG16, resulting in faster and more accurate image analysis.

• Developed NLP pipelines with spaCy, Word2Vec, and NLTK to analyze clinical notes and extract structured medical entities like symptoms and medications, streamlining data processing for clinical research.

• Implemented early object detection systems using Faster R-CNN and custom Python scripts to identify anomalies in medical imaging, enhancing early detection capabilities in clinical settings. Machine Learning Intern Pinewood Analytics ( Jan 2014 – Dec 2014 )

• Developed image classification and object detection models using MATLAB, ImageJ, and CNN frameworks.

• Preprocessed and annotated medical imaging datasets with Python tools and optimized model performance using PCA and hyperparameter tuning.

• Built and deployed a prototype system that improved biomarker detection accuracy, reducing false negatives in medical imaging.

Certification

AWS Certified Cloud Practitioner

CISCO Certified CyberOps Associate

EDUCATION

Rice University – Houston, TX

M.S. in Computer Science (AI/ML focus) – 2014

B.S. in Computer Science – 2012

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