Job Description: Principal AI Leader – Process / Industrial Automation
Role Summary
The Principal AI Leader will define and execute an AI-first engineering strategy for Process / Industrial Automation, advancing product development, engineering productivity, product intelligence, and enterprise AI adoption. The role will move AI beyond isolated pilots into secure, scalable, governed solutions that deliver measurable business outcomes. Combining technical leadership, strategic vision, and execution discipline, the successful candidate will modernize traditional engineering practices into an AI-driven operating model. They will partner with engineering, product management, architecture, and business leaders to identify high-value AI opportunities, build scalable solutions, and accelerate adoption across the organization.
Key Responsibilities
- Strategy, Roadmap, and Operating Model: Define the AI vision, maturity roadmap, operating model, investment priorities, and adoption plan for Process / Industrial Automation.
- AI-Enabled Product Development: Accelerate AI adoption across requirements, architecture, design, coding, testing, DevSecOps, security, documentation, release management, SRE, and operations.
- AI-Powered Product Capabilities: Partner with product management and engineering to embed AI copilots, conversational AI, predictive analytics, digital twins, autonomous agents, edge AI, knowledge assistants, and industrial AI capabilities into products.
- Reusable AI Platforms and Frameworks: Establish scalable capabilities such as LLM gateways, RAG platforms, vector databases, prompt libraries, model registries, agent frameworks, AI SDKs, evaluation frameworks, monitoring, and AI marketplaces.
- Responsible AI, Security, and Governance: Set standards for privacy, security, data governance, prompt governance, explainability, hallucination control, AI audits, compliance, ethics, risk management, and cost governance.
- Organizational Enablement and Adoption: Build AI capability through evangelists, champions, guilds, communities of practice, internal certifications, hackathons, innovation labs, reusable assets, and adoption programs.
Required Qualifications
- Experience and Domain Depth: 15–20+ years in software engineering, including 8–10+ years in AI/ML, data platforms, industrial automation, or related technology domains.
- Generative and Agentic AI: 3–5+ years of experience with GenAI and LLMs, plus 2+ years working with agentic AI capabilities.
- Engineering and Product Expertise: Deep expertise across software engineering, product engineering, AI/ML, and the needs of Process / Industrial Automation products, customer workflows, reliability expectations, and operating environments.
- Enterprise Transformation Leadership: Proven ability to lead enterprise-scale AI, GenAI, ML, automation, or digital transformation initiatives from strategy through production adoption.
- Stakeholder Influence: Demonstrated ability to influence senior stakeholders across engineering, product, IT, architecture, cybersecurity, data, legal, operations, finance, and executive leadership.
- Execution and Outcomes: Strong track record of defining roadmaps, building reusable platforms, leading cross-functional execution, and delivering measurable business outcomes.
Technical Skills
- AI Platform Engineering: LLM gateways, RAG platforms, vector databases, model registries, agent frameworks, AI SDKs, prompt libraries, evaluation frameworks, AI marketplaces, and reusable AI services.
- Generative AI and Agentic AI: Generative AI, OpenAI, LLMs, prompt engineering, agentic AI, multi-agent systems, MCP, knowledge graphs, conversational AI, and autonomous agents.
- RAG and Knowledge Systems: RAG architecture, knowledge databases, embeddings, retrieval pipelines, Chroma, pgvector, Pinecone, Azure AI Search, vector indexing, and grounding strategies.
- AI/ML Engineering and Operations: Machine learning, MLOps, LLMOps, MLflow, LangSmith, model evaluation, model monitoring, AI observability, production AI operations, and lifecycle automation.
- Cloud AI Platforms: Azure AI Foundry, Azure OpenAI, Azure ML, Azure AI Search, Google Vertex AI, cloud-native AI deployment, enterprise integration, scalability, and secure platform operations.
- Software Engineering and DevSecOps: Cloud-native engineering, containers, Kubernetes, DevSecOps, SRE, secure software development, release automation, enterprise integration, and AI-assisted software delivery.
- AI Engineering Tools and Developer Productivity: GitHub Copilot, Microsoft Copilot, LLM APIs, AI SDKs, prompt testing tools, open code, specKit, GrillMe, token reduction approaches such as RTK or equivalent, and developer workflow automation.
- Context Engineering and Responsible AI Operations: Context engineering strategies such as .GitHub copilot-instructions or .cursor rules, prompt governance, hallucination control, AI monitoring, responsible AI, security, privacy, compliance, and cost governance.
Leadership and Business Skills
- AI Strategy and Transformation: Lead enterprise AI strategy, transformation roadmaps, product thinking, innovation management, and executive communication.
- Business Impact and Execution: Convert AI opportunities into measurable gains in engineering productivity, product differentiation, customer value, financial performance, and operational efficiency. Drive product-led initiatives from concept through execution, adoption, and measurable impact.
- Stakeholder Leadership: Engage and influence stakeholders across product, engineering, architecture, Enterprise IT, cybersecurity, data, legal, compliance, operations, finance, and business leadership.
- Investment, ROI, and Cost Governance: Oversee ROI tracking, vendor management, platform investments, budget planning, licensing, infrastructure optimization, and responsible AI cost governance.
- Enterprise AI Platforms and Ecosystems: Establish enterprise-grade RAG, LLMOps, and AI governance frameworks. Integrate external agents and MCP servers into enterprise AI workflows and apply NVIDIA technologies and ecosystem capabilities where they strengthen AI solution development.
Success Measures
- Engineering: Higher developer productivity, shorter lead times, increased deployment frequency, lower defect density, improved MTTR, and broader AI-assisted engineering adoption. Create scalable, production-ready AI frameworks and agentic AI capabilities across the engineering lifecycle.
- Product: Stronger AI feature adoption, higher customer satisfaction, increased feature usage, better prediction and response quality, improved retention, and measurable revenue contribution from AI capabilities.
- People: Greater AI proficiency, increased certifications, more prompt library contributions, higher training completion, stronger hackathon participation, and greater innovation output.
- Financial: Reduced engineering costs, improved AI ROI, optimized infrastructure and cloud usage, better license efficiency, and disciplined responsible AI cost management.