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Senior Engineering Leader | ML/AI, Platforms, Cloud Infrastructure, AWS
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Summary
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Engineering leader with more than 20 years of experience building and scaling cloud-native, distributed systems and ML platforms, and leading teams across infrastructure, platform, and product engineering. Proven track record owning mission-critical systems with high availability and customer impact, from ML inference and training to developer platforms, CI/CD, and SRE.

Known for people-first, systems-oriented leadership that creates clarity, trust, and durable processes. Brings strong system-level thinking to architecture and organizational design, enabling teams to move quickly without sacrificing reliability. Pragmatic decision-maker and advocate for generative engineering cultures that balance autonomy with accountability.

Works at the intersection of technology strategy, organizational design, and long-term platform thinking.

Core Strengths
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  • Engineering Leadership: Org design, hiring and retention, career frameworks, mentorship
  • Platforms and Infrastructure: Distributed systems, developer platforms, CI/CD, SRE
  • ML Systems: MLOps, model serving, feature pipelines, ethical and auditable AI
  • Cloud: AWS-native architectures, scalability, reliability, cost efficiency

Experience
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Director of Engineering, Core Platform, Machinify (2026-04 to present)
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Leads Core Platform engineering at Machinify, the platform-as-a-product layer beneath the company’s healthcare AI products. Owns the self-service APIs, shared services, infrastructure, and developer tooling that the product verticals build on, spanning feature flagging, job processing, event-driven architecture, observability, the data query layer, and the application framework. Machinify serves more than 60 health plans covering more than 160 million lives.

  • Responsible for Spark workload management, critical path API availability and performance
  • Drove visibility into engineering effectiveness and proposed organizational change to increase overall organizational capacity and velocity
  • Unblocked security upgrades to critical runtime systems by converging 3 interdependent layers, the VM, the Java runtime, and third-party dependencies including Jetty and Spark, on mutually compatible versions
  • Prioritized resiliency work, including dynamic rate throttling, to raise service availability
  • Drove roadmap planning discussions
  • Owns the Core Platform technical roadmap and the tradeoffs between feature velocity, technical debt, and platform evolution
  • Manages, mentors, and hires backend engineers, and runs career development and performance conversations
  • Partners with product engineering teams as an internal consultant, treating platform adoption and API usability as product metrics

Senior Director of Engineering, Splice (2024-04 to 2026-03)
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Led a 20-person organization at Splice spanning Core ML Operations (inference and training), foundational APIs, and Site Reliability Engineering. Owned the shared platforms behind product velocity, system reliability, and customer availability.

  • Built AWS-native ML platforms with GitOps-backed CI/CD, autoscaling inference, and a centralized ML Model Gateway
  • Delivered end-to-end feature extraction pipelines with model and data lineage, versioning, and auditability to support ethical AI at scale
  • Partnered with ML research, product, and design to ship customer-facing AI capabilities, including multimodal Describe a Sound
  • Established and scaled engineering career leveling to support transparent growth and performance conversations
  • Built the mechanisms that put AI coding assistants into daily engineering practice, and shipped AI capabilities into the product
  • Hired, mentored, and retained engineers and managers while growing organizational capacity

Career Break (2023-06 to 2024-03)
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Planned time away from full-time work.

Software Development Manager, Amazon Web Services (2020 to 2023-06)
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Engineering manager for AWS Lambda and AWS CodeCatalyst, contributing to core platform capabilities used by millions of customers.

  • Shipped major Lambda features including SnapStart and response streaming
  • Led CI/CD coordination across 12 teams and more than 100 engineers, improving deployment visibility, reducing operational risk, and accelerating release cycles
  • Defined metrics and mechanisms to surface per-component failure rates, regional skew, and batch sizes, materially improving operational response time
  • Scaled team capacity through hiring and org design while modernizing a legacy Go codebase into gRPC-based microservices
  • Drove engineering excellence initiatives, including the Lambda Correction of Error Bar Raiser program

Engineering Manager, Machine Learning, Lyft (2019 to 2020)
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Engineering Manager for Lyft Forecasting and ML Training / Flyte teams, supporting both near-real-time and long-range business forecasting.

  • Secured funding and led replacement of legacy systems with Spark-based pipelines, open-sourced as Fugue
  • Migrated near-real-time forecasting to Kafka, Flink, Druid, and Seldon for improved scalability and durability
  • Instituted press-release-driven development to clarify objectives, align stakeholders, and close accountability gaps
  • Fostered a strong culture of learning, collaboration, and delivery across engineering and data science

The Flyte platform supported more than 100,000 monthly workflow executions for business-critical workloads.

  • Maintained full team retention during organizational restructuring
  • Improved on-call experience and escalation policies, reducing operational burden while increasing feedback cycles
  • Prioritized observability and systemic reliability improvements to enable greater customer self-service

Director of Site Reliability Engineering, ShiftLeft (2016 to 2019)
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Founded and led Site Reliability Engineering for ShiftLeft (now Qwiet AI), designing and evolving a Kafka-mediated microservices platform in Go.

  • Established standardized observability, structured logging, and fail-fast configuration across all services
  • Built core service integrations with Kafka, Kinesis, S3, Prometheus, Vault, GitHub, and Segment
  • Delivered internal tooling, including a secure React-based operations console
  • Partnered with operations to ensure cluster-agnostic deployments and long-term platform scalability

Engineering Manager, NodeSource (2016)
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Led infrastructure and platform engineering for NodeSource Certified Modules, an AWS-hosted SaaS offering.

  • Automated CloudFormation-based provisioning and ephemeral build pipelines
  • Enabled AWS Marketplace integrations and multi-cloud VM builds using Packer

Engineering Manager and Senior Computer Scientist, Adobe (2007 to 2015)
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Progressed from Senior Computer Scientist to Engineering Manager at Adobe, leading teams delivering Creative Cloud Assets and collaborative media products.

  • Led large-scale platform rewrites and data migrations with zero downtime
  • Migrated services to Docker-based deployments with continuous delivery
  • Designed real-time collaborative systems using Scala, Akka, and Apache Camel
  • Defined REST APIs, caching strategies, and observability across multiple product lines

Community and Industry Involvement
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Education
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University of Washington, Certificate Program
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Algorithms and Data Structures (completed with distinction)

University of Notre Dame, Ph.D. Candidate
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Economics, with focus on Computational Economics and the history of artificial intelligence in economic theory. Research presented at the American Economic Association Annual Meeting (1996).

Bucknell University, Bachelor of Arts
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Economics (minor in English), cum laude, Phi Beta Kappa

Patents
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