Senior Enterprise Architect

OnTrac

. Define and own OnTrac’s target-state architecture for an AI-native logistics platform on GCP .

Posted 10/22/2026full-timeRemote • United StatesSenior💰 $156,000 - $195,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in defining and architecting AI-native logistics platforms on Google Cloud Platform (GCP), with a strong focus on multi-agent systems, API management, and data engineering for AI. Proven ability to lead architectural initiatives, mentor engineering teams, and deliver production-quality solutions in the logistics industry.

Highest-signal resume keywords

GCP Architecture ExpertiseAgentic AI KnowledgeAPI Management and IntegrationData Engineering for AIDevOps and MLOps Skills

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI-Native Logistics ArchitectureMulti-Agent Systems DesignData ModelingBatch and Stream ProcessingETL/ELTAPI Design and IntegrationGenerative AI SolutionsCloud RunKubernetesTerraform

Soft Skills

Architectural LeadershipMentoringCollaborationPrioritization

Tools & Technologies

Google Cloud PlatformApigeeKafkaPub/SubVertex AIBigQueryDataflowMuleSoftKongCI/CD

Certifications & Qualifications

Google Cloud Professional Certifications

Industry Keywords

Last Mile DeliveryParcel LogisticsLogistics TechnologyDelivery OperationsParcel Lifecycle

Tech Stack

Tools & technologies

AzureBigQueryCloudETLGoogle Cloud PlatformGRPCKafkaKubernetesTerraform

About the role

Key responsibilities & impact

Define and own OnTrac’s target-state architecture for an AI-native logistics platform on GCP
Design and build agentic and multi-agent systems, including orchestration, planning, tool use, memory, human-in-the-loop controls, and A2A communication
Architect the MCP server ecosystem exposing logistics services and enterprise data as governed tools for AI agents
Design AI-ready data platforms with batch and real-time pipelines, lakehouse/warehouse architectures, feature stores, and vector stores
Define a canonical data model and event taxonomy for logistics entities, with data quality, lineage, and governance
Define API and integration architecture using REST, gRPC, asynchronous APIs, OpenAPI, AsyncAPI, Apigee, Pub/Sub, and Kafka
Apply and document distributed, AI, and agentic architecture patterns and publish reference architectures
Build proofs of concept, prototypes, and reference implementations; write production-quality code with engineering teams
Apply AI to order intake, address validation, geocoding, route optimization, ETA prediction, exception management, control tower operations, driver/dispatch assistance, and customer experiences
Lead architecture and design reviews, author ADRs, contribute to governance and technology selection, and mentor engineers
Report to the CTO and collaborate across platform, core, and domain engineering teams

Requirements

What you’ll need

Bachelor’s degree or equivalent and 10 years of relevant experience in software engineering, data engineering, or solution architecture
At least 7 years in an architecture role
2 years of hands-on generative AI or LLM solution delivery
3 years of hands-on experience designing, building, and running production solutions on Google Cloud Platform (GCP)
3 years of experience in the last mile delivery, parcel, or logistics industry, supporting delivery operations technology
Experience designing and implementing enterprise API management and integration platforms such as Apigee, MuleSoft, Kong, or Azure API Management
Deep hands-on knowledge of agentic AI, agent frameworks, multi-agent orchestration, tool and function calling, agent memory, MCP, and A2A protocols
Expert knowledge of GCP architecture and AI/data services including Vertex AI, Gemini, BigQuery, Dataflow, Pub/Sub, GKE, Cloud Run, and Apigee
Expert knowledge of data engineering for AI, including data modeling, batch and stream processing, ETL/ELT, CDC, lakehouse design, feature/vector stores, data quality, and governance
Strong knowledge of API management and integration, API standards, gateways, OAuth 2.0, OIDC, Kafka, and Pub/Sub
Strong DevOps, MLOps, and LLMOps skills, including Terraform, CI/CD, Kubernetes, model/prompt versioning, automated evaluation, and LLM observability
Understanding of last mile delivery and parcel logistics, including parcel lifecycle, scan events, network planning, routing, geospatial data, and driver/carrier operations
Ability to balance hands-on engineering with architectural leadership and prioritize multiple initiatives
Google Cloud Professional certifications and experience establishing an AI center of excellence, agent platform, or reference architectures are strongly preferred

Benefits

Comp & perks

Medical, dental, and vision insurance
Life and short- and long-term disability coverage
401(k) retirement savings plan with company match
Flex vacation in eligible states, with accruals up to 96 hours for first year of employment and tenure-based increases up to 160 hours
Two floating holidays per year
Paid sick leave
Six paid company holidays
Two weeks paid pregnancy disability leave
Four weeks paid parental bonding leave
Wellness and employee assistance programs
Bonus, commission, or other incentive compensation of up to a 25% Bonus Target may be available