Technical Architect - Forward Deployed Engineering

Who We Are & Why Join Us

Avathon is the leading Industrial AI autonomy platform, helping customers across heavy industries - energy, mining, manufacturing, aerospace, defense, and logistics - accelerate the journey toward autonomous operations. Our platform is built on a Computational Knowledge Graph foundation that contextualizes and connects operational data across siloed systems, bringing together time series, structured, unstructured, and machine vision data to power AI-driven applications in asset performance management, supply chain intelligence, visual AI, and global trade management. With capabilities spanning digital twins, normal behavior modeling, natural language processing, and computer vision, Avathon delivers real-time predictive intelligence and agentic decision-making at industrial scale.

Cutting-Edge AI Innovation - Join a team at the forefront of AI, developing groundbreaking solutions that shape the future. High-Growth Environment - Thrive in a fast-scaling startup where agility, collaboration, and rapid professional growth are the norm. Meaningful Impact - Work on AI-driven projects that drive real change across industries and improve lives.

Learn more at: avathon.com

Role Overview

The Technical Architect is the senior technical authority embedded within Avathon's Forward Deployed Engineering team — the person who translates complex customer environments into scalable, production-grade AI architectures and then owns their delivery. You sit at the intersection of deep platform expertise and industrial domain knowledge, architecting how Avathon's Computational Knowledge Graph, Model Hub, Agent Foundry, and Visual AI (VAIA) capabilities fit into each customer's existing OT/IT landscape and workflow reality.

This is an implementation role, not a pre-sales role. Where the Solutions Architect maps the possibility, the Technical Architect builds the system. You define integration strategy, design agentic AI workflows, govern solution quality, and hold technical accountability from kickoff through production handoff — working Monday through Thursday at client sites alongside FDE engineers, customer IT and OT teams, and plant operations. You own the hardest architectural decisions across multi-customer engagements and feed those patterns back into Avathon's delivery playbooks and platform evolution.

Key Responsibilities

Solution Architecture & Workflow Design

• Own end-to-end solution architecture for enterprise AI deployments — designing how Avathon's platform capabilities (CKG, Model Hub, Agent Foundry, VAIA) map to each customer's operational workflows, data environment, and business objectives

• Architect and configure Avathon's Computational Knowledge Graph for each customer environment — defining data models, constraint representations, ontology mappings, and the decisioning topology that unifies siloed operational systems (SCADA, ERP, CMMS, PLM, historians)

• Design generative and agentic AI workflow orchestration on Avathon's Agent Foundry — defining agent roles, tool integrations, escalation logic, and human-in-the-loop decision points aligned to each customer's operational processes

• Architect Visual AI (VAIA) deployments for customers requiring computer vision and multi-modal AI — scoping camera infrastructure, model selection, inference pipeline design, and integration with existing asset monitoring systems

• Evaluate and prescribe integration patterns for industrial data sources — SCADA, OPC-UA/DA, MQTT, process historians (OSIsoft PI, Aspen IP.21, Honeywell Uniformance), ERP, CMMS — ensuring reliable, low-latency data flow into Avathon's AI pipeline

• Develop reference architectures and reusable design patterns from each engagement that reduce delivery risk and accelerate future implementations across similar environments

Technical Leadership & Governance

• Serve as the senior technical authority across FDE engagements — reviewing solution designs, setting engineering standards, and resolving architectural conflicts before they reach the client

• Define and enforce technical quality standards: data schema conventions, API contracts, security posture, scalability thresholds, model versioning, and disaster recovery requirements

• Partner with Avathon's core engineering and data science teams to ensure customer deployment architectures align with platform roadmap and long-term maintainability

• Identify and mitigate technical risk early — surfacing integration gaps, data quality issues, infrastructure constraints, and model performance risks before they impact delivery timelines

• Mentor Forward Deployed AI Engineers on architectural best practices, system design patterns, and production engineering standards — raising the technical bar of the FDE practice

AI Evaluation & Production Operations

• Define and implement evaluation frameworks for AI systems deployed at client sites — benchmarking model performance, assessing output quality, and establishing acceptance criteria that give engineering and operations teams confidence before go-live

• Design AIOps observability stacks for production AI deployments — establishing monitoring, alerting, and drift detection frameworks that give Avathon and client teams full visibility into model health, inference latency, and data pipeline reliability in live operational environments

• Specify model performance thresholds, confidence scoring standards, and feedback loops that allow production AI systems to be continuously improved without disrupting operations

• Drive end-to-end security architecture for industrial AI deployments: network segmentation, OT/IT boundary controls, data encryption, role-based access, and compliance with industrial cybersecurity standards (IEC 62443, NIST CSF)

• Establish hyperscaler infrastructure architecture (GCP, AWS, Oracle Cloud) for cloud-connected deployments — including edge-to-cloud data flows, model serving infrastructure, and hybrid connectivity for constrained plant environments

Customer Engagement & Advisory

• Act as the primary technical point of contact for senior customer stakeholders — IT leadership, chief engineers, OT architects, and CTO-level sponsors — throughout the engagement lifecycle

• Lead technical discovery and architecture workshops with customer IT, OT, and engineering teams — translating operational requirements, workflow constraints, and data realities into a coherent, implementable system design

• Translate complex architectural decisions into clear, business-relevant communication for non-technical executive audiences — linking technical choices to operational outcomes and risk

• Advise customers on OT/IT convergence strategy, industrial data governance, and the platform changes required to sustain AI-driven operations at scale

• Capture architectural learnings and integration patterns from each engagement and contribute them to Avathon's delivery playbooks, accelerating future implementations across similar industry environments

Preferred Skills

• Deep hands-on experience designing and deploying enterprise AI or data platform solutions in industrial, OT, or critical infrastructure environments — with direct ownership of production architecture

• Strong command of generative and agentic AI architectures — LLM integration, agent orchestration, RAG pipelines, tool use, and the design patterns required to make AI systems reliable and safe in high-stakes industrial contexts

• Proficiency in industrial data integration: MQTT, OPC-UA/DA, REST/GraphQL APIs, Kafka, Spark, and historian connectors — with practical experience validating signal quality and tag mappings in live plant environments

• Hands-on experience with AI/ML model deployment and serving infrastructure: containerization (Docker, Kubernetes), MLOps pipelines, real-time inference, and model monitoring at industrial scale

• Familiarity with industrial control systems and SCADA architecture — and the constraints they impose on data latency, network topology, and application design

• Strong cloud platform architecture skills across AWS, GCP, or Oracle Cloud — alongside edge and on-premise deployment models common in OT environments

• Solid programming foundation in Python; ability to read and review production code across the stack and work alongside engineering teams during integration and debugging

• Experience with graph data models, knowledge representation, or semantic data integration — particularly as it relates to connecting heterogeneous industrial data sources

• Familiarity with computer vision systems and multi-modal AI — camera infrastructure, inference pipelines, and integration with asset monitoring or quality inspection workflows

Qualifications

• 8+ years of experience in software engineering, systems architecture, or technical delivery — with at least 3 years in a lead architect or principal engineer role with direct delivery accountability

• Demonstrated track record of owning architecture for large-scale, production enterprise AI or industrial systems — ideally in energy, manufacturing, mining, aerospace, or defense

• Certification or demonstrated hands-on experience in data science, machine learning, or AI systems engineering

• Bachelor's degree in Computer Science, Engineering, or a related technical field; Master's preferred

• An active U.S. Government security clearance is a strong plus

• Willingness and ability to travel up to 40%; based in Pleasanton, CA or Austin, TX
• Must be authorized to work in the United States without current or future company sponsorship

Compensation & Benefits

• California Base Salary:  $170,000 - $220,000

• Performance bonus tied to account growth and delivery outcomes

• Equity in the form of stock options

• Comprehensive health, dental, and vision coverage

• 401(k) participation

• Generous PTO and company holidays

• Ongoing learning and development opportunities