Senior Enterprise Architect

OnTrac

. Define and own the target-state architecture for OnTrac’s 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

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Demonstrates expertise in defining and architecting AI-native logistics platforms on Google Cloud Platform (GCP), with a strong focus on agentic AI systems, API management, and data engineering for AI applications. Proven ability to lead architectural design, mentor engineering teams, and implement enterprise-level solutions in the logistics industry.

Highest-signal resume keywords

Google Cloud Platform (GCP) ArchitectureAgentic AI and Multi-Agent SystemsAPI Management and IntegrationData Engineering for AIDevOps and MLOps Skills

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Hard Skills

Software EngineeringSolution ArchitectureData EngineeringGenerative AIAPI Design StandardsData ModelingBatch and Stream ProcessingETL/ELTTerraformKubernetes

Soft Skills

LeadershipCollaborationMentoringPrioritizationProblem-Solving

Tools & Technologies

ApigeeKafkaPub/SubBigQueryDataflowGKECloud RunVertex AIGeminiMuleSoft

Certifications & Qualifications

Google Cloud Professional Certifications

Industry Keywords

LogisticsLast Mile DeliveryParcel LifecycleRoute PlanningScanningTrackingDriver OperationsGeospatial DataDelivery OperationsAI Center of Excellence

Tech Stack

Tools & technologies

AzureBigQueryCloudETLGoogle Cloud PlatformGRPCKafkaKubernetesMicroservicesReactTerraform

About the role

Key responsibilities & impact

Define and own the target-state architecture for OnTrac’s AI-native logistics platform on GCP
Design and build agentic and multi-agent systems, including orchestration, planning, tool use, memory/state management, human-in-the-loop controls, and A2A communication
Architect the MCP server ecosystem exposing logistics services and enterprise data as governed, reusable tools for AI agents
Design AI-ready data platforms with batch and real-time streaming pipelines, lakehouse/warehouse architectures, feature stores, and vector stores
Define a canonical unified data model and event taxonomy for orders, shipments, scan events, locations, containers, and drivers
Define API and integration architecture using API-first design, REST, gRPC, asynchronous APIs, OpenAPI, AsyncAPI, Apigee, Pub/Sub, and Kafka
Apply and document distributed and AI architecture patterns, including microservices, domain-driven design, event sourcing, CQRS, saga, ReAct, planner-executor, supervisor/router agents, and advanced RAG
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, delivery promise and ETA prediction, exception management, control tower operations, driver/dispatch assistance, and conversational 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
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 technology for delivery operations such as route planning, sortation, scanning, tracking, or driver and dispatch systems
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, including Google ADK, LangGraph, CrewAI, multi-agent orchestration, tool and function calling, agent memory, Model Context Protocol (MCP), and agent-to-agent (A2A) protocols
Expert knowledge of GCP architecture and AI/data services, including Vertex AI, Gemini, Vertex AI Vector Search, 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 data governance
Strong knowledge of API management and integration, including API design standards, gateways, OAuth 2.0, OIDC, Kafka, and Pub/Sub
Strong DevOps, MLOps, and LLMOps skills, including Terraform, CI/CD tooling, Kubernetes, model and prompt versioning, automated evaluation, and LLM observability tools
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 states other than CA, CO, IL, MA, MT, and NE, with accruals up to 96 hours for first year of employment with tenure-based increases up to 160 hours
Two (2) floating holidays per year
Paid sick leave
Six (6) paid company holidays
Two (2) weeks paid pregnancy disability leave
Four (4) weeks paid parental bonding leave
Additional wellness and employee assistance programs
Bonus, commission, or other incentive compensation of up to a 25% Bonus Target