Top Tech Jobs & Startup Jobs in Seattle, WA

Leads and mentors application engineers while designing Java and Spring Boot microservices. Oversees application modernization, VM-to-container migration, GCP and GKE deployments, Kubernetes troubleshooting, CI/CD, testing, database integration, observability, and distributed systems. Coordinates technical dependencies across a large application portfolio and collaborates with platform, DevOps, security, and architecture teams. Uses AI-assisted engineering tools to accelerate development, modernization, testing, documentation, and automation.

Designs and maintains Azure enterprise data infrastructure using Terraform and CI/CD pipelines. Implements multi-plane architectures, hybrid Azure-Google Cloud networking, secure platform services, Kubernetes workloads, identity and access controls, and centralized monitoring. The role supports Airflow, Milvus, Neo4j, GPU workloads, and healthcare-compliant environments with HIPAA, PHI, encryption, data residency, and secure SDLC requirements.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.

Designs and builds data ingestion, feature engineering, analytics, and machine learning pipelines using healthcare and other structured or unstructured data. Develops, tunes, deploys, monitors, and documents predictive models and enterprise data products. Collaborates with data scientists, engineers, product managers, and stakeholders while ensuring data quality, reliability, and production engineering standards.

Build and maintain full-stack applications that deliver AI/ML insights through backend services, REST APIs, containerized microservices, and high-performance front-end interfaces. Responsibilities include cloud-native development on Google Cloud or Azure, database management, testing, CI/CD automation, security, troubleshooting, and Agile collaboration.

Leads the architecture, design, and implementation of scalable GCP and Databricks data lakehouse platforms. Designs real-time and batch pipelines, data models, governance, cataloging, security, and performance optimization. Advises clients, translates business requirements into technical solutions, leads project teams, mentors engineers, reviews architectures, and balances strategic planning with hands-on proof-of-concept and troubleshooting work.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Owns public-sector sales and revenue growth across State, Local, and Higher Education accounts. Responsibilities include managing the full sales lifecycle, developing account strategies, owning P&L and bookings targets, building executive relationships, partnering with Google Cloud, scoping modernization projects, drafting Statements of Work, and supporting regional marketing. The role focuses on cloud, data, AI, and analytics modernization opportunities, particularly across California agencies and institutions.

Owns global payroll, benefits administration, HRIS operations, compliance, audits, and total rewards analytics across the U.S., Canada, U.K., and Europe. The role manages payroll vendors and benefits programs, leads open enrollment, ensures multi-jurisdictional compliance, optimizes HR systems, reconciles benefits costs, and mentors offshore People Operations staff.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.