- Location
- Boise, ID,US, US
- Workplace
- Hybrid
- Type
- Full-time
- Department
- Engineering
- Seniority
- Lead
- Source
- Eightfold
Description
Architect AI Solutions & Platforms: Define and evolve enterprise AI architecture patterns (cloud/hybrid, data pipelines, MLOps) for large-scale generative AI and ML workloads. Innovate and Prototype: Lead rapid experimentation by building AI proof-of-concepts (e.g. chatbots, predictive analytics, AI assistants). Validate feasibility, performance, and cost; recommend scale-up or pivot decisions based on results. Assess Value & Risk: Identify high-impact use cases across functions (manufacturing, supply chain, R&D, etc.) and quantify business value. Simultaneously evaluate data security, IP protection, privacy, fairness, and compliance, ensuring responsible AI practices in all initiatives. Enable Responsible AI: Develop standards and guardrails for model selection, prompt/data engineering, and human-in-the-loop processes. Advise on red-teaming, adversarial testing, and monitoring to mitigate AI risks. Communicate Insights: Translate complex AI developments into concise briefings for executives and partners. Provide deep technical mentorship to engineering teams and mentor stakeholders on AI capabilities and limitations. Drive AI Strategy: Partner with AI governance and IT leadership to update AI strategy and policies. 10+ years delivering impact in AI/ML and architecture roles, including leadership contributions across enterprise environments. Proven success designing and deploying enterprise-scale AI/ML systems, with emphasis on generative AI and cloud-native ML platforms. Deep expertise across cloud ecosystems (Azure/AWS/GCP), data engineering, and full MLOps lifecycle automation. Strong command of modern AI/ML frameworks and tooling (PyTorch, TensorFlow, LangChain, vector databases) plus robust grounding in AI governance, security, privacy, and NIST AI RMF. Outstanding communicator skilled in executive-level storytelling, multi-functional influence, and quickly adopting and operationalizing emerging AI technologies. Experience in semiconductor or manufacturing industries (understanding of MES, OT/IT integration, IP protection). Hands-on coding and prototyping ability in Python or similar, with familiarity of frontend technologies (for AI UX demos). Advanced degree in Computer Science, Engineering, or related field. Relevant certifications (e.g. Azure AI Engineer, Certified AI Professional). History of presenting at industry events or leading AI communities.
