// resume

Richard Landy

Head of Software at BrightSpot Automation

Experience

2023 — present

Owner of IMPEL, BrightSpot’s imaging and inspection application — grown from a single-shot prototype on modified consumer cameras into a production platform for inline manufacturing QC.
Architected its multiprocessing pub/sub core: concurrent control of industrial cameras and power supplies, with processing offloaded to a subprocess pool and a CUDA-enabled machine learning pipeline.
Built EL and PL capture orchestration — arrays of power supplies under active monitoring to hold LED luminous intensity stable through long exposures.
Integrated IMPEL into inline manufacturing QC: MES integration via PLC, REST API control, and a range of supported peripherals.
Delivered quantitative analysis capabilities — multi-camera rig calibration for image stitching and precise feature measurement — plus the backend reliability and continuous-delivery work to run it all in production.
Led the software effort on an inspection system selected as an R&D 100 Award finalist.

2020 — 2023

Led a major US insurer’s lift-and-shift of backend services to a cloud platform, from MVP and early project work through scaled implementation — one of five architects on the ~150-person project’s leadership team, owning the data pipeline and ingestion services.
Managed three engineering teams of eight, leading one day to day as a senior developer; promoted from Software Engineer Team Lead to Software Engineering Manager along the way.
Implemented the client’s new data science environment end to end: ETL, the data science workbench, and the model training and validation flow.
Developed a COVID-19 vaccine documentation pipeline for a major US drugstore chain, spanning service development and data ingestion.

2017 — 2020

Owned software architecture and technical direction for the company’s data product marketplace.
Developed, refined, and deployed models for household energy-usage profiling and predictive maintenance of grid assets.
Built ETL pipelines aggregating energy grid data into the analytics warehouse, with autoscaling infrastructure for the data environment.

2016 — 2017

Built internal deployment tooling and metrics dashboards for production service monitoring.
Prototyped Docker-based inference environments for model serving — early containerization work, when that was still a novel approach.

// domain

Computer visionOptical inspectionNIR imagingOptical metrologyOptics & lightingEdge deploymentSystem architecture

// practice

Architecture & strategic planningPrototype → productionCustomer discovery & analysis

// engineering

PythonC++PyTorch · CUDAOpenCV · NumPyPyQtDockerAWSDjangoJava · Spring BootGit · code reviewAgentic AI toolingWindows development

BA Computer Science · Columbia University · Artificial Intelligence concentration

2016

R&D 100 Award finalist · BrightSpot Automation

2026

TechStar award · Accenture Technology

2023

Manager's Choice Award · IBM Watson Health

2016

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