Principal AI Engineer | Data Science


Hybrid/Remote, Corporate Headquarters, Chicago, IL | 2026 - Present

Summary

As a Principal AI Engineer & Data Scientist at Redwood Logistics, I operate at the forefront of artificial intelligence, data science, and full stack engineering — turning complex, high-stakes business challenges into elegant, scalable technology solutions.

My work centers on the full lifecycle of AI-driven product development — from early-stage data exploration and proof-of-concept research to the design and deployment of production-grade applications. I specialize in building integrated data systems and intelligent automation pipelines that don't just solve problems, but fundamentally redefine how work gets done.

With a dual focus on technical depth and business impact, I architect machine learning models, AI-powered tools, and end-to-end software applications that deliver quantifiable results — streamlining operations, accelerating decision-making, and replacing thousands of hours of manual effort with smart, self-sustaining systems.

At the core of my approach is a simple but powerful belief: the best technology is the kind you don't have to think about — it just works, scales, and creates value from day one.

Strategic Impact

  • Delivering Results That Matter: With a track record of architecting and deploying multiple concurrent, high-complexity data science and engineering projects, I consistently translate technical depth into measurable business wins — from automated pipelines that eliminate operational bottlenecks to AI-driven systems that generate real, bottom-line impact.
  • A Champion for Intelligent Innovation: Known enterprise-wide as a thought leader in AI engineering and strategic adoption, I bring a rare combination of deep technical expertise and rapid execution — designing, building, and shipping proof-of-concepts and MVP applications that evolve into robust, production-grade solutions.
  • Full Stack AI Application Development & Rapid Prototyping: Fluent across the entire software development stack, I design and engineer end-to-end AI-powered applications at speed — integrating large language models, machine learning APIs, and intelligent automation frameworks into scalable, production-ready products that deliver immediate and lasting business value.
  • Intelligent Data Systems & AI Pipeline Architecture: I architect and engineer sophisticated AI-driven data systems that unify disparate sources into coherent, self-sustaining pipelines — transforming raw, complex data into structured intelligence that continuously feeds machine learning models, powers predictive analytics, and enables autonomous decision-making at enterprise scale.
  • Machine Learning & Generative AI Engineering: From prompt engineering and fine-tuning large language models to designing and deploying custom machine learning solutions, I own the full AI development lifecycle — building intelligent systems that go far beyond the notebook, engineered to operate reliably in production, adapt over time, and generate compounding value across the organization.

Role Details

  • Redefining What's Possible with AI: I don't just implement AI — I reimagine how it can fundamentally change the way organizations operate. By staying ahead of the rapidly evolving AI landscape and continuously experimenting with emerging tools and frameworks, I consistently bring solutions to the table that challenge conventional thinking and unlock capabilities the business didn't know were within reach.
  • From Idea to Intelligence: I have a rare ability to take an ambiguous business problem, rapidly deconstruct it into its technical components, and engineer a working AI-powered solution in a fraction of the time traditional development cycles allow — compressing months of R&D into days without sacrificing quality, scalability, or long-term maintainability.
  • Architecting the AI-Powered Enterprise: I design and build the intelligent backbone that modern logistics operations depend on — engineering interconnected AI systems, automated decision engines, and real-time data platforms that work seamlessly together to eliminate inefficiencies, reduce costs, and create competitive advantages at scale.
  • Turning Unstructured Chaos into Structured Intelligence: The logistics industry generates an enormous volume of unstructured, noisy, and inconsistent data. I build the NLP and AI systems that make sense of it all — extracting clean, actionable signals from documents, communications, and operational data streams that would otherwise go untapped.
  • Engineering AI That Scales With the Business: Building a model that works is one thing — building one that continues to perform reliably as data volumes grow, user demands shift, and business conditions evolve is another. I engineer AI systems with scalability and longevity at their core, ensuring that every solution I deliver is as powerful on day one thousand as it is on day one.
  • Accelerating Human Decision-Making with Machine Intelligence: I build AI tools that don't replace human judgment — they sharpen it. By surfacing the right information at the right time through intelligent automation, predictive modeling, and real-time analytics, I empower logistics teams and executives to make faster, more confident, and more informed decisions across every level of the organization.
  • Closing the Gap Between Data Science and Production Engineering: One of the most persistent challenges in AI development is the chasm between experimental data science and reliable production software. I live and operate in that gap — applying rigorous software engineering discipline to machine learning workflows to ensure that models, pipelines, and AI applications are built to production standards from day one.
  • Building AI Ecosystems, Not Just Applications: My work goes beyond individual tools or point solutions. I design holistic AI ecosystems where data pipelines, machine learning models, APIs, automation layers, and user-facing applications are thoughtfully connected — creating a unified intelligence infrastructure that compounds in value over time as each component feeds and strengthens the others.
  • Democratizing AI Across the Organization: I believe powerful AI shouldn't be locked inside the data science team. I actively work to surface AI capabilities across the broader organization — building intuitive, accessible tools and internal platforms that put the power of machine learning and intelligent automation directly into the hands of the people who need it most.
  • Engineering Efficiency at a Scale That's Hard to Ignore: The systems I build don't just improve processes — they eliminate entire categories of manual work. By combining machine learning, intelligent automation, and smart system design, I consistently deliver solutions that reclaim thousands of hours of operational effort, freeing teams to focus on higher-value work that drives the business forward.