Software Engineering Manager - Sight & Sense; AI​/ML in Dearborn

Job Description We’re Hiring:
Software Engineering Manager – Sight & Sense (AI/ML)

Location:

Dearborn, MI (Onsite) Local and Near by Location

yEXP - 15

• ts Bachelor’s degree in Computer Science, AI/ML, or a related technical fiel
• d.Leadership
  
  Experience:
  
  3+ years of experience directly managing software engineering teams, with a proven track record of hiring, mentoring, and retaining talen
• t.Technical Background: 5+ years of experience in software engineering, specifically involving AI/ML, Computer Vision, or complex data-driven product
• s.Systems Knowledge:
  Deep understanding of the machine learning lifecycle (data engineering, model training, inference, and monitoring
• ).Agile Proficiency:
  Extensive experience leading teams in Agile environments, managing sprints, and optimizing delivery velocit

• Do Engineering Leadership & Mentorship:
  Lead, coach, and grow a multi-disciplinary team of software and data engineers. Foster a collaborative environment focused on continuous improvement and professional developme
• nt.
  
  Technical Delivery:
  Own the software development lifecycle for Sight and Sense products. Ensure the team delivers high-quality, maintainable code that meets project milestones and performance benchmarks and results in a reliable and highly scalable produ
• ct.
  
  Architecture & Strategy:
  Provide technical oversight for software and hardware architecture, MLOps pipelines, technology stack selection, and controls integration. Balance the need for rapid innovation with the stability required for 24/7 manufacturing environmen
• ts.
  
  Cross-Functional Collaboration:
  
  Partner closely with Product Managers and stakeholders to translate manufacturing challenges into technical roadmaps and executable engineering backlo
• gs.
  
  Platform Integration:
  Ensure all vision products are built for seamless integration with centralized data and operating platforms, adhering to a "build once, scale everywhere" philosop
• hy.
  
  Operational Excellence:
  Drive engineering best practices, including automated testing, CI/CD, and robust monitoring for models deployed at the "edge" (on-site factory hardwar
• e).Innovation:
  Drive innovation by integrating Agentic workflows and AI-assisted engineering tools to maximize team productivity and software qual
• ity Deployment Oversight:
  Work with site-level stakeholders to ensure technical solutions account for real-world variables such as factory lighting, line speeds, and network constrain

ts.