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
Avathon is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws.
Avathon is committed to providing reasonable accommodations throughout the recruiting process. If you need a reasonable accommodation, please contact us to discuss how we can assist you.
