nano2macro

Career

Experience

From doctoral research in computational science to AI product leadership in industry.

  1. Smarter Technologies

    Aug 2025 – Present

    Technical Product Manager — Customer Delivery · Pittsburgh, PA

    Forward-deployed AI solution architecture and customer delivery — architecting end-to-end agentic AI systems with human-in-the-loop control for enterprise revenue-cycle workflows, and leading their implementation across nationwide providers.

    Forward-Deployed AI Solution Architecture

    • Agentic AI System Design (HITL) — Architected end-to-end agentic AI systems with human-in-the-loop control points, automating complex RCM workflows while maintaining auditability and compliance. The platform processes enterprise-scale workloads — 150K+ daily eligibility checks, 1K+ prior authorizations, 50K+ claims/day, and 2K+ financial postings — with structured escalation for exceptions.
    • Agent Architecture & Orchestration — Designed multi-step agent workflows with structured state management, tool/API integrations, and retrieval-augmented reasoning, enabling dynamic task planning, execution, and failure handling.
    • Analytics & Observability — Built customer-facing dashboards (Amazon QuickSight) to monitor agent performance and failure modes, enabling real-time intervention and continuous improvement.

    Product, Delivery & Customer Execution

    • Technical Implementation Lead — Led deployment of agentic AI platforms across 5+ nationwide providers in engagements totaling $4.5M+, reaching >80% automation at >90% average accuracy.
    • Cross-Functional Delivery — Directed 15+ forward-deployed engineers, managing sprint cadence, risks, validation, and release readiness across concurrent deployments.
    • Product Configuration & Requirements — Translated customer workflows into PRDs, technical requirements, and acceptance criteria, defining scope and prioritization by operational impact.
    • Workflow Discovery & Solution Architecture — Partnered with SMEs, IT stakeholders, and engineering teams to define scalable workflows, identify constraints, and determine platform extensions.

  2. Deloitte

    Jan 2024 – Aug 2025

    Applied AI Senior Consultant (Technical Lead) · Pittsburgh, PA

    Led technical delivery of enterprise AI, analytics, and optimization platforms for Fortune 100 and S&P 100 clients. Promoted from Applied AI Consultant to Senior within the first year.

    Fortune 100 Energy & Chemical Client

    • Analytics Platform Design & Productization — Led a 12-member cross-functional team to transform advanced analytics and optimization workflows into a modular, scalable platform with reusable components across enterprise decision-making.
    • Platform Impact — Delivered an AI-driven commodity-optimization platform generating $75M/year, and a GenAI copilot that reduced case setup time by >70%.
    • Delivery Leadership — Owned roadmap execution, sprint delivery, and release governance; aligned business and technical stakeholders through 35+ workshops to drive adoption.

    Enterprise Transformation (S&P 100 Client)

    • Program & Product Operations — Led coordination of 6 interdependent workstreams within a $1.4B transformation, managing dependencies, risks, and execution alignment to deliver $120M+ in annual impact.

    AI Strategy & Innovation Portfolio

    • Proposals — Led development of AI strategies and technical proposals, securing $10M+ in new projects across sectors.
    • Product Strategy — Defined product vision and commercialization pathways for a specialized chemical company, combining technical feasibility with market insight to surface capability gaps and high-impact opportunities.

  3. Carnegie Mellon University

    Jan 2019 – Dec 2023

    PhD Researcher — Mechanical Engineering · Pittsburgh, PA

    Doctoral research in high-performance modeling of complex systems across scales — molecular simulation, statistical thermodynamics, and machine learning for physical systems. Thesis: “Excess Entropy Scaling in Molecular Simulations: Transport in Soft and Active Systems.”

    • High-Performance Computing Method Development — Created a method to address finite-size statistical errors in molecular simulations, optimizing computation and data handling for large-scale datasets.
    • Deep Learning for Physical Properties — Built neural networks to predict physical properties from experimental data, with hyperparameter optimization and robustness testing.
    • Authored peer-reviewed publications (including Nature Scientific Reports and a selected journal cover) and presented at international conferences; received best-poster awards.

  4. University of Toronto

    2016 – 2018

    Lecturer & MASc, Mechanical Engineering · Toronto, Canada

    Master's research in interfacial thermodynamics and evaporation, alongside lecturing and teaching in the Department of Mechanical & Industrial Engineering.

    • Lecturer — Designed and taught Introduction to Mechanical & Industrial Engineering (MIE191) to 165 first-year students; 4.6/5 evaluation across 62 responses.
    • Teaching — Teaching assistant for engineering analysis, dynamics, and differential-equations courses; led SolidWorks/ANSYS and research-writing workshops.
    • Research — MASc research on surface characterization and interfacial transport via the zeta adsorption isotherm.

Education

  • PhD, Mechanical Engineering (Computational Science)

    Carnegie Mellon University · Pittsburgh, PA

    2023

  • MASc, Mechanical Engineering

    University of Toronto · Toronto, Canada

    2018

  • BASc, Mechanical Engineering (High Honours)

    University of Toronto · Toronto, Canada

    2016