Senior Enterprise Architect

OnTrac; Oregon; Hybrid; USD 180,000 - 225,000; Full time

Benefits offered by this job

Medical, dental, vision

401(k) with match

Flexible vacation

Paid holidays & leave

Life and disability insurance

Wellness programs

Job summary

OnTrac is seeking a Senior Enterprise Architect to lead the AI-native logistics platform on Google Cloud Platform, driving target architectures and tiered data services. You will define agentic systems, MCP server ecosystems, and scalable data pipelines to support real-time operations, ETL/ELT, and AI workflows across the delivery network.

You will partner with platform, core, and domain teams, mentor engineers, and ensure governance, security, and production readiness while advancing AI

Qualifications

• Bachelor's degree or equivalent with 10 years in software engineering, data engineering, or solution architecture, including at least 7 years in an architecture role and 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 last mile delivery, parcel, or logistics tech supporting delivery operations such as route planning, sortation, scanning, tracking, or dispatch systems.
• Experience designing and implementing enterprise API management and integration platforms (Apigee, MuleSoft, Kong, or Azure API Management).
• Deep knowledge of agentic AI, including agent frameworks, multi‑agent orchestration, tool calling, MCP, and A2A protocols.
• Expert knowledge of GCP, Vertex AI, Vector Search, BigQuery, Dataflow, Pub/Sub, GKE, Cloud Run, Apigee.
• Data engineering for AI including data modeling, ETL/ELT, CDC, lakehouse design, feature stores, and data governance.
• Strong API management and integration skills including OAuth2, OIDC, Kafka, Pub/Sub, and exposing AI-agent tools.

Responsibilities

• Define and own the target‑state architecture for OnTrac's AI-native logistics platform on GCP across platform services, core services, domain apps, and AI layers.
• Design and build agentic and multi‑agent systems with orchestration, planning and tool use, memory management, and A2A communication.
• Architect the MCP server ecosystem exposing orders, shipments, tracking, and location data as governed, reusable tools for AI agents.
• Design AI-ready data platforms with batch/real-time pipelines, lakehouse, and vector stores using BigQuery, Dataflow, Pub/Sub, and Vertex AI.
• Define unified data models and event taxonomies for orders and deliveries with contracts, data quality, lineage, and governance.
• Define API and integration architecture with REST, gRPC, OpenAPI contracts, and API management via Apigee; support event-driven integration.
• Publish ADRs, lead design reviews, mentor engineers, and drive governance for AI technology choices.
• Apply AI to order intake, routing, ETA, and operations control in last‑mile delivery and customer experiences.

Skills

GCP architecture

LLM frameworks

Agent frameworks

API management

DevOps / MLOps / LLMOps

Data engineering for AI

Delivery logistics knowledge

Terraform / CI/CD

Education

Bachelor's degree or equivalent

BS in CS or related field

Tools

Apigee

MuleSoft

Kong

Kubernetes

Vertex AI / BigQuery

Job description

OnTrac is hiring a Senior Enterprise Architect!

Are you eager to join a dynamic and expanding company where you can both learn and make a meaningful impact? If you possess a strong sense of empathy, enjoy assisting others, thrive in a fast-paced environment, and excel at problem-solving.

Location

REMOTE : This position may be performed remotely in states where the company is authorized to employ individuals.

Compensation

The expected starting base pay range for this position is $180,000 - $225,000, with full potential base salary range over a successful candidate's tenure in the position of $180,000 - $270,000 . Actual compensation will be determined based on experience, skills, internal equity, and other job-related factors. This position may also be eligible for bonus, commission, or other incentive compensation in accordance with the terms of the applicable plan of up to a 25% Bonus Target.

Job Description

Reporting to the CTO, this position is a hands‑on, highly technical architect responsible for designing and building the AI‑native platform, data, and integration architecture that powers OnTrac's last mile delivery network. Working across platform, core, and domain engineering teams, this position defines the target‑state architecture on Google Cloud Platform (GCP) for generative AI, agentic workflows, and real‑time data, proves designs through proofs of concept and reference implementations, and partners with engineers to take them into production. The role brings deep expertise in LLM frameworks, Model Context Protocol (MCP), multi‑agent systems, data engineering, API management, architecture patterns, and DevOps and LLMOps, and keeps the organization at the forefront of rapidly evolving AI technology.

Benefits

• 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

Requirements

• Bachelor's degree or equivalent and 10 years of relevant experience in software engineering, data engineering, or solution architecture, including at least 7 years in an architecture role and 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 agent frameworks (such as Google ADK, LangGraph, or 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 its AI and data services, such as 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, change data capture (CDC), lakehouse design, feature and vector stores, data quality, and data governance.
• Strong knowledge of API management and integration, including API design standards, gateways, OAuth 2.0 and OIDC, event streaming with Kafka and Pub/Sub, and exposing APIs as tools for AI agents.
• Strong DevOps, MLOps, and LLMOps skills, including Terraform, CI/CD tooling (such as GitHub Actions, Azure DevOps, or Cloud Build), Kubernetes, model and prompt versioning, automated evaluation, and LLM observability tools.
• Understanding of last mile delivery and parcel logistics, including the parcel lifecycle, scan events, network planning, routing, geospatial data, and driver and carrier operations, and where AI creates value in each.
• Ability to work in a fast‑paced environment, balance hands‑on engineering with architectural leadership, and prioritize multiple initiatives.
• Google Cloud Professional certifications, such as Professional Cloud Architect, Professional Machine Learning Engineer, or Professional Data Engineer and experience establishing an AI center of excellence , agent platform, or reference architectures and standards in a product-led technology organization are strongly preferred.

Responsibilities

• Define and own the target‑state architecture for OnTrac's AI-native logistics platform on GCP, spanning shared platform services, reusable core services, domain applications, and the AI and agent layer that runs across them.
• Design and build agentic and multi‑agent systems, including agent orchestration, planning and tool use, memory and state management, human-in-the-loop controls, and agent-to-agent (A2A) communication.
• Architect the MCP server ecosystem that exposes core logistics services (orders, shipments, tracking, locations, routing) and enterprise data as governed, reusable tools for internal and external AI agents.
• Design AI-ready data platforms, including batch and real‑time streaming pipelines, lakehouse and warehouse architectures, feature stores, and vector stores, using services such as BigQuery, Dataflow, Pub/Sub, Spanner, AlloyDB, and Vertex AI Vector Search.
• Define a canonical, unified data model and event taxonomy for orders, shipments, scan events, locations, containers, and drivers, published as versioned contracts and as semantic context for LLMs and agents, with data quality, lineage, and governance built in.
• Define the API and integration architecture, including API-first design, REST, gRPC, and asynchronous APIs, OpenAPI and AsyncAPI contracts, API management through Apigee, and event-driven integration over Pub/Sub and Kafka, serving applications and AI agents alike.
• Apply and document architecture patterns for distributed and AI systems, including microservices, domain-driven design, event sourcing, CQRS, and saga, alongside agentic patterns such as ReAct, planner‑executor, supervisor and router agents, and advanced RAG, and publish reference architectures for engineering teams.
• Build proofs of concept, prototypes, and reference implementations hands‑on to validate new models, agent frameworks, and AI tools, de-risk designs, and move quickly from experiment to production; write production‑quality code alongside engineering teams.
• Apply AI to last mile delivery capabilities, including intelligent order intake, address validation and geocoding, route optimization, delivery promise and ETA prediction, exception management, AI‑powered control tower operations, driver and dispatch assistance, and conversational customer experiences.
• Lead architecture and design reviews, author architecture decision records (ADRs), contribute to architecture governance and AI technology selection, and mentor engineers across teams.

Disclosures

• Washington state employees are eligible for up to 56 hours of paid sick leave annually.
• The salary range above represents the national range for this position. The salary range may be inclusive of several career levels at OnTrac, and the actual base offered may vary depending on several factors including, but not limited to: geographic location, candidate experience and qualifications, job-related skills and competencies, market alignment, and financial considerations.
• Compensation decisions are made based on the specific circumstances of each hire to ensure fair and competitive pay.
• This job posting is anticipated to remain open for at least 30 days from the date of posting.

OnTrac is proud to be an Equal Opportunity EmployerLasership, Inc. dba OnTrac Final Mile with its affiliates, including OnTrac Logistics, Inc. (collectively, "OnTrac" or the "Company") is an equal opportunity employer.

We value diversity and welcome applications from individuals of all backgrounds, abilities, and experiences.

We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or age.

Join us in our commitment to creating a diverse and inclusive workplace.