Chris Cordaro Applied AI · ML systems · Production

Raleigh, NC

Chris Cordaro, AI and ML engineer.

I’ve spent twelve years building data and ML software. Most recently that’s a production RAG and multi-agent SaaS I designed and shipped alone. Before that: applied ML (forecasting, risk scoring, NLP) inside a federal agency, and a 400-user analytics platform I led the build of. Mostly Python, deployed in containers, tested in CI.

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Proof

  1. <2 min

    Opportunity research, down from ~2 hours

    A RAG copilot over 50,000+ federal solicitations: hybrid full-text and vector retrieval, intent classification, streamed answers with citations. Built solo, in production.

  2. ~6 min

    15-page intelligence reports, down from 8+ hours

    A four-phase multi-agent pipeline (research, analyze, write, review) that streams its progress while it runs.

  3. 30%

    More accurate workforce forecasts at the DoD Inspector General

    LSTM forecasting, XGBoost attrition scoring, and genetic-algorithm allocation. Manual analysis fell 60% and time-to-hire 10%.

  4. 400+

    Users on a self-service analytics platform

    A modern-data-stack migration over 40+ sprints: a 35% cost and scalability gain and 98% on-time delivery.

Recognition

  • UkraineOversight.gov, which I designed and delivered, was cited by CNN, C-SPAN, and Congress
  • Glenn/Roth Award for Exemplary Service
  • 30+ open agent tools, 150+ installs across three CLIs

Selected work

Three projects: a product I built alone, an ML platform I shipped inside a federal agency, and a data team and platform I ran for six years.

  1. Build

    Scout: an AI research platform for federal contracting, built solo

    A RAG and multi-agent SaaS that finds federal contract opportunities and writes the competitive research on them.

    The problem

    Opportunities are spread across four government sources (SAM.gov, USASpending, FPDS, SBA) with conflicting metadata and heavy overlap. Analysts spent hours per pursuit re-reading solicitations and assembling competitive intelligence by hand.

    What I built

    Scheduled ingestion pulls all four sources, deduplicates them in four tiers, and validates the result. Each opportunity gets a composite score that blends structured signals with vector similarity. Users ask a RAG copilot (hybrid full-text and pgvector retrieval, intent classification) and get streamed, cited answers from 50,000+ live solicitations. On request, a four-phase agent pipeline (14 coordinated agent personas, shared persistent memory) produces a full competitive-intelligence report.

    How it’s built

    Python / FastAPI · PostgreSQL + pgvector · Docker · Cloudflare Zero Trust, Workers, R2 · Claude and Gemini APIs · GitHub Actions, pytest, Ruff, mypy

  2. Model

    Workforce forecasting and allocation at the DoD Inspector General

    The Inspector General planned its workforce in spreadsheets. I led the build of the ML platform that replaced them and the rollout that got people using it.

    The problem

    Planning was manual. Forecasts were spreadsheet estimates, attrition was noticed after it happened, and allocation had no model behind it. Analysts and leadership also had to trust the model’s output before they would act on it.

    What I built

    LSTM networks forecast demand, XGBoost scores attrition risk, a genetic algorithm handles constrained allocation, and BERT-based NLP classifies positions and skills. We delivered it over 24 two-week sprints against a 300+ story backlog, with each deliverable traced to a mission outcome. Results went into self-service dashboards so program offices could answer their own questions.

    How it’s built

    Python · TensorFlow, scikit-learn, XGBoost · SQL · Tableau · Agile with requirements traceability

  3. Lead

    Running a data team and platform at the DoD Inspector General

    Six years on one program: I ran a 15-person data team and moved its platform to a modern data stack while deliveries stayed on schedule.

    The problem

    A multi-year analytics program whose platform needed modernizing while the team kept shipping to the people who relied on it.

    What I did

    I directed a 15-person team of data scientists, engineers, and BI analysts; led a modern-data-stack migration over 40+ sprints; built a self-service platform for 400+ users; and founded a Data Science Center of Excellence for methods, governance, and mentoring. I also designed and delivered UkraineOversight.gov, the public dashboards for oversight of federal Ukraine funding. Today I lead a 16-person team on a Military Health System contract and still write code: ML and analytics pipelines for pharmaceutical supply-chain analysis, and an automated reporting pipeline that saves ~20 hours a month.

    How it’s built

    Tableau · Spark · Python, SQL · Agile at 98% on-time delivery

How I build

  1. Understand the data and the user first

    Find out what decision the user is making and whether the data can support it before writing code.

  2. Prototype the risky part

    Build whatever is most likely to fail first, usually retrieval quality, model accuracy, or throughput.

  3. Ship in increments, with the plumbing

    Tests, CI, containers, and observability from the first increment, and I stay in the code.

  4. Measure adoption

    It’s finished when people use it and a number moves, such as hours saved per task.

Experience

Twelve years across data, ML, and product.

  1. 2025 – Present

    Knowesis Inc.

    AI Engineering Lead & Program Manager

    Architected and shipped Scout, a production RAG and multi-agent SaaS, solo, while leading a 16-person Military Health System contract.

  2. 2024 – 2025

    DoD Office of Inspector General

    Enterprise Business Innovation Manager

    Led the applied-ML workforce platform (LSTM, XGBoost, BERT) and the agency’s data modernization program.

  3. 2019 – 2024

    Knowesis Inc. — DoD Inspector General

    Technical Lead & Program Manager, Data Platform

    Directed a 15-person analytics team, the modern-data-stack migration, and UkraineOversight.gov.

  4. 2018 – 2019

    Knowesis Inc. — FEMA / Defense Health Agency

    Solution Architect

    Cross-platform disaster-recovery data system on AWS; NLP-driven document classification platform.

  5. 2013 – 2018

    Atkins (SNC-Lavalin) — FEMA

    Data Manager

    Enterprise data-validation frameworks behind $300M+ in FEMA funding, 0.05% error rate.

Download resume (PDF) →

Capabilities

  1. Hands-on engineering

    Python, TypeScript, SQL · FastAPI, Next.js, React · Docker, Kubernetes · GitHub Actions, pytest, Ruff, mypy · AWS, Azure, GCP, Cloudflare

  2. Applied ML and GenAI

    Forecasting (LSTM), risk scoring (XGBoost), NLP (BERT), optimization (genetic algorithms) · RAG and hybrid retrieval · embeddings, pgvector · multi-agent orchestration · MCP · Claude and Gemini APIs · evaluation and validation frameworks

  3. Data platforms

    ETL and multi-source ingestion · deduplication and validation · PostgreSQL, Spark, Databricks · Tableau, Power BI

  4. Solution architecture

    Zero Trust · secure by design in regulated environments · cost and latency budgets · observability

  5. Delivery and leadership

    Agile (PMI-ACP) · requirements traceability · 98% on-time delivery · mentorship · centers of excellence · change management

Certifications PMI Agile Certified Practitioner (PMI-ACP) — 2022 · Tableau Certified Associate — 2019 · Data Science Professional Certificate, HarvardX — 2021 · DataWalk Technical Professional — 2022

About

Chris Cordaro

Most of my work starts with a question someone can’t yet answer from their data. I work out which numbers matter and build the software that produces them. I also build on my own time: this site, a daily AI briefing that researches, writes, and publishes itself, and agent tools that other engineers install.

Read the daily briefing →Agent tools on GitHub →

Federal and defense

Active SECRET clearance. Twelve years in DoD, DHA, and FEMA environments. Fluent in FedRAMP, CMMC, Zero Trust, FAR/DFARS, and OTA acquisition.

Contact

Let’s talk about what you’re building.

I’m looking for applied AI and ML engineering roles where I own a system end to end, from data ingestion to the product people use. I’m also glad to look over the architecture of something you’re building.

[email protected]
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