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Ekele Ogbadu

Lead AI/ML Engineer · Agentic AI, RAG, LLM Systems, Secure AI Platforms

Lead AI/ML Engineer with 12+ years of software engineering experience and 4+ years of applied machine learning experience supporting mission-critical defense, cyber, and AI modernization programs. Leads directorate-level AI/ML initiatives across LLM-enabled workflows, semantic retrieval, tagging, data pipelines, evaluation systems, and operational AI adoption. Ph.D. candidate in Computer Science specializing in multimodal AI, grounded language understanding, and human-robot interaction, with hands-on experience across NLP, retrieval, computer vision, sensor fusion, and production ML systems.

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Experience

  • Lead AI/ML Engineer

    Booz Allen Hamilton — Annapolis Junction, MD

    June 2026 — Present
    • Lead AI/ML training, tool development, platform enablement, and secure transition of AI capabilities into mission environments.
    • Conduct AI/ML training for personnel and contribute to enterprise training approaches that standardize how users learn, evaluate, and apply AI-enabled tools.
    • Develop AI-enabled tools for the AI Gallery, translating mission needs into reusable capabilities for training, knowledge discovery, experimentation, and operational support.
    • Build AI platform tools and agentic workflows using LangChain/LangGraph patterns and MCP-style tool integration to support secure tool access and workflow orchestration.
    • Design LLM-enabled and RAG-style workflows for tagging, semantic retrieval, ontology-driven classification, and vector search using Python, embeddings, AWS Bedrock, and OpenSearch.
    • Create gold set generation workflows, retrieval evaluation loops, and prompt evaluation methods to improve tagging quality, retrieval relevance, and trust in AI-enabled tools.
    • Partner with stakeholders, engineers, and platform teams to identify AI use cases, define implementation paths, and deliver secure AI/ML capabilities.

  • Senior AI/ML Engineer

    Booz Allen Hamilton — Annapolis Junction, MD

    Sep 2025 — May 2026
    • Delivered the team's tagging pipeline end to end, completing a long-standing capability after rapidly learning a large codebase, designing the solution, and integrating it into the broader application.
    • Designed an ontology-driven tagging architecture that structured how unstructured content was classified, tagged, and surfaced across the application.
    • Developed automated data ingestion, enrichment, and feature pipelines in Python to process large-scale document and signal collections.
    • Architected RAG-style tagging and semantic retrieval workflows using embeddings, AWS Bedrock, OpenSearch, vector search patterns, and LLM-enabled ranking.
    • Built gold set generation workflows and MLOps-aligned evaluation loops to improve tagging accuracy, retrieval relevance, and model quality.

  • Senior Agile Engineer (ML Programs)

    Booz Allen Hamilton — Annapolis Junction, MD

    Jul 2024 — Sep 2025
    • Led release operations and technical delivery coordination across multiple teams on a $200M mission program.
    • Drove delivery improvements that reduced average days to completion from 167.3 to 36, and cut blocked-release time from 250.5 days to 0.
    • Led daily scrums across four sites and presented release metrics, bottlenecks, and improvement opportunities during PI Planning and other program forums.

  • AI/ML Graduate Research Assistant

    UMBC — Interactive Robotics & Learning Lab — Baltimore, MD

    May 2023 — Present
    • Conduct applied research in multimodal grounding and grounded language understanding for human-robot interaction, integrating text, images, and sensor data including 3D point clouds, audio, and ROS bag files.
    • Developed SCOUT++ from the original SCOUT dataset by cleaning, restructuring, and aligning multimodal data into 11,000+ timestamp-aligned instruction-image pairs.
    • Designed end-to-end ML pipelines for preprocessing, synchronization, model training, and evaluation using PyTorch, TensorFlow, Pandas, NumPy, OpenCV, and ROS.
    • Evaluated neural and GPT-based approaches for grounded instruction understanding, including ambiguity handling, contextual reasoning, and clarification behavior.
    • First author on peer-reviewed research presented at the AAAI 2025 Fall Symposium, with additional first-author work accepted to IEEE RO-MAN 2026.

  • Software Engineering Team Lead

    Prescient Edge Corporation — McLean, VA

    Aug 2023 — Jul 2024
    • Led engineers maintaining the MINOTAUR platform across C++, Java, React, and TypeScript, providing technical direction, code review standards, and onboarding support — improving junior engineer ramp-up time by 60%.

  • Machine Learning Engineer

    Prescient Edge Corporation — McLean, VA

    May 2022 — Aug 2023
    • Developed object detection and computer vision capabilities for Hull Identification Number recognition using YOLO, TensorFlow, and OpenCV across infrared and visible imagery.
    • Built real-time sensor-fusion pipelines combining radar and camera inputs to track high-speed vessels and automate camera tilt and zoom.

  • Software Developer

    Prescient Edge Corporation — McLean, VA

    Feb 2021 — May 2022
    • Built and maintained mission-critical software deployed on U.S. Coast Guard rescue aircraft, integrating C++, Java, CI/CD, UDP/TCP, MIL-STD-1553, ARINC-429, and simulation tooling for hardware–software validation.

  • Aviation Administrationman

    United States Navy

    Oct 2014 — Oct 2018
    • Supervised night-shift aviation maintenance administration and managed 900+ physical and digital records, using SQL and enterprise systems including NALCOMIS OMA and SYBASE to track equipment lifecycle, readiness data, and maintenance schedules.
    • Served as official VAQ-132 Command Webmaster, developing and maintaining the command website.

  • Founder / Independent Software Developer

    Early entrepreneurial experience

    2007 — 2018
    • Built and launched web platforms, mobile applications, games, matching platforms, and business-driven software products across iOS, Android, and the web, including ventures through Born Royal LLC.

Education

  • Ph.D. Candidate, Computer Science

    University of Maryland, Baltimore County

    Expected Dec 2027
    • Research: multimodal NLP and perception for real-time human–robot interaction.

  • M.S., Computer Science

    University of Maryland, Baltimore County

    May 2024
    • Thesis: “Statistical Language and Neural Network Models: Classifying Human Instructions in Situated Robot Command.” Advised by Prof. Cynthia Matuszek.

  • B.S., Computer Science (Data Science concentration)

    University of Maryland, Baltimore County

    May 2022

  • B.A., Mathematics

    University of Maryland, Baltimore County

    May 2022

  • Undergraduate Coursework, Computer Science

    Edinboro University of Pennsylvania

    2003 — 2007
    • Completed 110 credits toward a B.S. in CS before leaving to build and launch entrepreneurial software products.

Technical Skills

  • Languages

    • Python, C++, Rust, Java, JavaScript, TypeScript, SQL, Bash

  • AI/ML & Deep Learning

    • Machine Learning, Deep Learning, NLP, Computer Vision, Multimodal AI, Information Retrieval, Semantic Search, Vector Search, Retrieval-Augmented Generation (RAG), Embeddings, Classification, Object Detection, Sensor Fusion, Grounded Language Understanding, Human-Robot Interaction
    • PyTorch, TensorFlow, scikit-learn, OpenCV

  • LLM & Agentic AI Systems

    • LLM Applications, AI Agents, Multi-Step AI Workflows, Tool-Using Agents, Agentic AI, LangChain, LangGraph, MCP Servers, Prompt Engineering, Prompt Evaluation, AI Workflow Orchestration, LLM Tool Integration, RAG Pipelines, Knowledge Discovery Systems

  • MLOps, Evaluation & Production AI

    • Model Evaluation, Retrieval Evaluation, Gold Set Generation, Model Monitoring, Experiment Tracking, Dataset Versioning, ML Pipelines, CI/CD for ML Systems, Production ML Workflows, Human-in-the-Loop Evaluation, Responsible AI, AI Safety, AI Governance

  • Data, Search & Platforms

    • OpenSearch, Vector Databases, PostgreSQL, MongoDB, Pandas, NumPy, Large-Scale Data Processing, Data Ingestion, Data Enrichment, Ontology-Driven Classification, Metadata Extraction

  • Cloud & Infrastructure

    • AWS, AWS Bedrock, EC2, S3, SageMaker, Docker, Kubernetes, REST APIs, Microservices, CI/CD, Secure AI Platform Development, ROS

  • Engineering & Leadership

    • System Design, AI Platform Engineering, Technical Leadership, Cross-Functional Delivery, Agile/Scrum, Release Management, Stakeholder Engagement, Mission Engineering, Startup Product Development, Open-Source AI Projects, Git, GitHub/GitLab, Jupyter, React, Node.js

Certifications

  • AWS Certified Machine Learning — Specialty

    Amazon Web Services

  • AWS Certified Cloud Practitioner

    Amazon Web Services

  • CompTIA Security+ ce

    CompTIA

  • Certified SAFe® 6 Practitioner

    Scaled Agile

  • Artificial Intelligence Engineer — Expert

    Booz Allen Hamilton

  • Artificial Intelligence Engineer — Practitioner

    Booz Allen Hamilton