Principal Systems Development Engineer
Indexed description
Key Responsibilities:
- Serve as the Product Owner and Enterprise Architect for Agentic AI enablement products and engineering platforms.
- Maintain deep technical expertise and hands-on knowledge of enterprise application architecture, cloud platforms, AI technologies, integration frameworks, and modern software engineering practices.
- Define product vision, strategy, roadmap, requirements, and success metrics for AI-powered engineering productivity solutions.
- Drive the transformation from traditional engineering processes to AI-native workflows leveraging intelligent assistants, AI agents, and automation.
- Lead the design and governance of Agentic AI ecosystems, including Model Context Protocol (MCP), agent orchestration, lifecycle management, security, and compliance.
- Partner with hardware, firmware, software, systems engineering, and business stakeholders to identify and prioritize high-value AI transformation opportunities.
- Develop enterprise data and integration strategies supporting Generative AI, RAG, semantic search, knowledge management, and autonomous workflows.
- Modernize legacy applications and drive adoption of scalable cloud-native engineering platforms.
- Establish technology standards, architecture principles, governance frameworks, and engineering best practices.
- Influence executive leadership and provide technical direction across multiple organizations.
- Master's or Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Computer Engineering, Information Systems, or a related technical field.
- 10+ years of experience in software engineering, systems development, platform engineering, product management, enterprise architecture, or related technical leadership roles.
- Proven experience serving as a Product Owner, Technical Product Leader, Enterprise Architect, or Principal Engineer for enterprise-scale platforms.
- Experience leading digital transformation, platform modernization, and engineering productivity initiatives.
- Strong expertise in cloud-native architectures, distributed systems, APIs, microservices, and enterprise integration patterns.
- Experience collaborating across hardware, firmware, software, and systems engineering organizations.
- Experience delivering Agentic AI, Generative AI, MCP, RAG, knowledge graph, or AI orchestration solutions.
- Experience building internal engineering productivity platforms and workflow automation solutions.
- Knowledge of PLM, ALM, DevOps, Engineering Lifecycle Management, and related engineering toolchains.
- Experience with Azure, Kubernetes, DevSecOps, Infrastructure as Code, and platform engineering practices.
- Master's degree in Engineering, Computer Science, Artificial Intelligent.
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