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📍 Seattle, WAehsan@ehsanshahidi.com ehsanshahidi

Staff full stack software engineer and product owner with a background spanning mechanical engineering, simulation, data engineering, and machine learning. I enjoy taking products from design to production launch and digging into the architecture that makes them fast.

Experience

  1. Staff Full Stack Software Engineer & Product Owner

    June 2022 — Present

    Seeq

    • Led the development of Enterprise Asset Monitoring, a new product suite, from design to production launch — tackling triage of time-series events, scalable event labeling, and incorporating user context. Now a primary revenue driver.
    • Performed a full API overhaul and redesigned the data architecture, transitioning from PostgreSQL to a custom in-house relational database, achieving a 15% reduction in load times.
    • Implemented advanced client caching strategies that streamlined large, time-intensive backend data updates.
    • Improved performance and user experience by leveraging GraphQL subscriptions and replacing frequent REST API calls with WebSocket connections.

  2. Senior Applied Research Engineer

    June 2021 — June 2022

    Seeq

    • Built a machine learning feedback system that allowed plant operators to triage and label time-series events.
    • Developed an open-source add-on for Seeq enabling users to define custom formula packages and generate synthetic time-series signals.
    • Enhanced the Docker and Kubernetes implementation for Seeq add-on tools, slimming images to support Seeq's transition towards a microservices architecture.

  3. Data Analytics Engineer

    March 2020 — June 2021

    TC Energy

    • Developed a pipeline for retrieving time-series data from a historian database and ingesting it into a data lake.
    • Reduced machine learning feature set generation latency from 8 hours to 2 hours while halving compute cost (10+ TB/day).
    • Built Asset Inventory, a graph database map of all company assets.
    • Engineered a novel method to increase pipeline throughput by 5% through ML-enhanced predictions of pressure drops, using tree-based and linear models alongside a new dimension reduction technique.

  4. Simulation Engineer

    Nov 2013 — March 2020

    TC Energy

    • Developed an automated finite-element model generator and Monte Carlo simulation to define acceptable vibration levels for almost any pipe geometry, using 200,000+ automatically built models.
    • Developed an Operator Training Simulator (OTS) and Computational Pipeline Monitoring system for pipeline and terminal projects.
    • Developed a novel computational leak detection method.

Education

M.Sc. in Mechanical Engineering

University of Alberta

Thesis: Simulation of complex multiphase fluids

B.Sc. in Mechanical Engineering

University of Tehran

Teaching

Skills

Languages & frameworks
PythonKotlinTypeScriptReactFlaskFastAPI
Databases & query languages
PostgreSQLRedisGraphQL
Cloud & DevOps
DockerKubernetesGitHub ActionsJFrogTeamCity CI/CD

Interests

Quantitative financePoker14th century music