Software Architect, AI

Niron Magnetics, Inc.•Minneapolis, MN

About The Position

Niron Magnetics is seeking a Software Architect, AI to define and evolve the technical architecture for AI‑driven systems across manufacturing, R&D, and business operations. This role will be responsible for setting architectural direction, enabling scalable and reliable AI solutions, and partnering cross‑functionally to ensure AI capabilities deliver real operational and business impact. This is a senior, hands‑on technical leadership role for someone excited to build foundational AI systems in a high‑growth, deep‑tech manufacturing environment.

Requirements

10+ years of experience in software engineering, data platforms, or AI/ML systems, with demonstrated architectural ownership.
In-depth experience building applications on top of large language models (OpenAI, Anthropic, open-weight models, or comparable) — including prompt design, function/tool calling, structured outputs, RAG architectures, and agent frameworks.
Demonstrated experience integrating software with enterprise systems via REST, GraphQL, message queues, webhooks, and ETL pipelines
Solid grounding in cloud architecture (Azure preferred), containerization (Docker), and at least one orchestration approach (Kubernetes and/or serverless preferred.).
Demonstrated skills in Python, Rust, and R
Strong understanding of modern AI/ML frameworks, data pipelines, cloud‑native architectures, and MLOps concepts.
Experience integrating AI systems with operational or industrial environments preferred.
Ability to balance near‑term delivery with long‑term architectural vision.
Excellent communication skills and comfort influencing without authority.
Strong problem‑solving skills and comfort operating in ambiguous, early‑stage environments.

Nice To Haves

Experience in manufacturing, industrial, or deep‑tech environments.
Advanced degree in Computer Science, Engineering, Data Science, or a related field.
Experience with edge AI, time‑series data, or physics‑informed ML.
Passion for clean tech, sustainable materials, or climate‑positive innovation.

Responsibilities

Define AI Architecture and Technical Strategy: Design and own the end‑to‑end architecture for AI and machine learning systems across data ingestion, model development, deployment, and monitoring.
Establish architectural patterns and standards for scalable, secure, and maintainable AI platforms.
Ensure AI systems integrate effectively with manufacturing systems (e.g., MES, ERP), laboratory tools, and enterprise software.
Enable AI‑Driven Innovation: Partner with Manufacturing, R&D, Quality, and Operations teams to identify high‑value AI use cases (e.g., process optimization, quality prediction, anomaly detection).
Translate business and technical requirements into robust AI system designs.
Guide model lifecycle management, including experimentation, deployment, retraining, and performance monitoring.
Technical Leadership and Enablement: Act as a technical authority and advisor on AI architecture, best practices, and trade‑offs.
Review designs, mentor engineers and data scientists, and raise the technical bar across teams.
Support build‑vs‑buy decisions and evaluate vendors, tools, and platforms.
Cross‑Functional Collaboration: Work closely with software, data, and infrastructure teams to ensure architectural alignment and execution.
Partner with IT and Security to ensure AI systems meet reliability, security, and compliance standards.
Communicate complex technical concepts clearly to non‑technical stakeholders.