Top Tech Jobs & Startup Jobs in Seattle, WA

Leads enterprise-scale Microsoft Hyper-V infrastructure engineering across global data centers. Designs resilient clusters, automates provisioning and compliance with PowerShell and DSC, develops C#/.NET orchestration services, benchmarks performance, troubleshoots full-stack infrastructure issues, evaluates server hardware, remediates vulnerabilities, documents architectures, and mentors engineers. The role also promotes validated, auditable use of enterprise AI capabilities in infrastructure operations.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Leads complex AI infrastructure technology programs from planning through delivery. Responsibilities include defining scope, timelines, resources, budgets, and deliverables; managing risks and dependencies; coordinating engineering, product, and business stakeholders; applying analytical reasoning; promoting agile and AI-assisted governance practices; and ensuring human validation, data sensitivity compliance, and alignment with business goals.

Leads the design, deployment, and operation of CDN production systems using Akamai and Cloudflare. Develops IaC and GitOps automation, network provisioning workflows, APIs, and potentially React interfaces. Drives system architecture, delivery pipelines, testing, security, reliability, and operational excellence. Coaches engineers through code reviews and technical decisions while promoting responsible AI-assisted development practices and validating AI-generated outputs for security, correctness, and performance.

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

Develops secure, scalable software solutions within an agile team. Responsibilities include application design, coding, testing, troubleshooting, architecture documentation, data analysis, visualization, and operational stability. The role also uses enterprise-authorized AI-assisted development tools responsibly, validates generated outputs, improves coding practices, and supports CI/CD, resiliency, and security standards.

Leads software engineering for an external partner API developer portal serving strategic banking API customers. Responsibilities include API platform delivery, collaboration with product and design leadership, enterprise AI-enabled engineering strategy, SDLC automation, governance, security, resiliency, stakeholder alignment, complex project oversight, and talent development across multiple technology organizations.

Leads infrastructure platform engineering within an agile team, delivering secure, scalable production software. Provides technical direction, reviews code, drives software development lifecycle governance, and promotes responsible AI-assisted engineering practices. Designs and troubleshoots systems independently while serving as a technical subject matter expert. The role emphasizes cloud-native infrastructure, automation, operational stability, and collaboration with technical teams, contractors, and vendors.

Lead software engineering for JPMorgan Chase’s API Marketplace within an agile team. Responsibilities include designing and delivering secure production code, reviewing and debugging applications, guiding technical teams and partners, influencing technology decisions, mentoring developers, contributing to SDLC frameworks, and building cloud-native distributed applications using modern development, testing, and deployment practices.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.