Description:
The AI Operations Engineer will identify, deploy, and scale practical AI solutions across engineering, manufacturing, project delivery, finance, recruiting, and operations. This person will translate operational challenges into high-value AI use cases and pilot workflows, then partner with technical teams to integrate successful solutions and measure their impact.
A key focus of the role will be identifying workflows and opportunities to expand ATLAS (AI Three-dimensional Modeling, Layouts, Analysis, and Specifications), Subcritical Systems’ central agentic platform for solving complex engineering problems. Built on a state-of-the-art knowledge layer, workflow agents, and tools, ATLAS enables teams to rapidly develop and deploy AI-enabled workflows.
KEY RESPONSIBILITIES
- Reporting to the System Engineer, own SSI’s AI implementation roadmap, prioritizing use cases based on business value, feasibility, and risk, and defining how they can be incorporated into ATLAS
- Map current processes and identify opportunities for improvement through AI, agents, and automation.
- Design, prototype, pilot, deploy, and scale reliable AI-enabled workflows for ATLAS, partnering with software engineers to harden, deploy, and scale successful solutions.
- Define requirements for integrating AI workflows with SSI’s existing software, systems, data, and operating processes, and partner with technical teams to implement them.
- Partner with engineering, operations, and business teams to deliver solutions that meet operational and technical requirements.
- Evaluate AI tools and recommend whether SSI should build, adopt, pilot, or defer them.
- Define success metrics and measure improvements in quality, speed, cost, throughput, and hours saved.
QUALIFICATIONS
Required
- Bachelor’s degree in engineering, computer science, information systems, business operations, or a related field, or equivalent demonstrated practical experience.
- 1+ years of hands-on experience building and implementing AI solutions in an operational environment.
- Working knowledge of generative AI, tool calling, retrieval-augmented generation, multi-agent workflows, data integration, and workflow orchestration.
- Demonstrated ability to use frontier AI models to design, build, test, and deploy agentic systems that automate real business and operational workflows, with a track record of taking AI solutions from problem definition through prototype, pilot, production deployment, and scaled adoption.
- Experience connecting AI agents to enterprise data, internal tools, APIs, databases, and operational systems.
- Experience mapping complex business processes, identifying high-value automation opportunities, and translating operational needs into technical requirements.
- Understanding of data protection, access controls, cybersecurity, responsible AI, and the safe deployment of AI within an enterprise environment.
- Demonstrated experience using and building agents on local models (e.g., Muse, Qwen, Gemma, etc.)
Preferred
- Experience deploying technology in a technically complex environment such as engineering, advanced manufacturing, energy, aerospace, nuclear, or industrial operations.
- Experience evaluating third-party AI tools and frameworks to support build, adopt, pilot, or defer decisions.
- Experience working in a fast-moving startup or rapid-development environment.