Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Software Engineering or related work experience.
OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Software Engineering or related work experience.
OR PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.
4+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
Demonstrated Principal-level technical leadership, cross-team influence, architecture ownership, and organization-scale impact.
Deep expertise in Generative AI, AI/ML fundamentals, LLM-based systems, model/tool selection, evaluation, optimization, and practical application in engineering environments.
Hands-on experience building, deploying, or integrating GenAI solutions such as agentic workflows, RAG/context-aware systems, developer tools, automation frameworks, or AI-enabled engineering platforms.
Proven ability to lead large-scale technical transformation across teams, influence without authority, shape architecture, and drive adoption in complex engineering organizations.
Strong understanding of the SDLC, including requirements, design, implementation, code review, testing, CI/CD, release readiness, security workflows, and productivity measurement.
Strong foundation in secure software development, responsible AI usage, data protection, governance, observability, and risk management for enterprise AI systems.
Experience in systems software, embedded software, platform software, or performance-sensitive engineering environments, with proficiency in languages such as C, C++, Python, or Go.
Excellent communication, collaboration, mentoring, and executive-facing storytelling skills, with the ability to translate technical depth into clear strategy and measurable impact.
Experience building AI agents, multi-agent workflows, MCP services, tool-calling systems, evaluation harnesses, prompt/skill libraries, or governed AI automation platforms.
Experience with AI-enabled developer productivity use cases such as code generation, code review, defect triage, log analysis, test generation, documentation, release validation, or security analysis.
Familiarity with embedded platforms, Linux, Android, RTOS, device drivers, BSP, SoC bring-up, hardware/software interfaces, or protocols such as I2C, SPI, UART, USB, or PCIe.
Experience with camera, multimedia, sensors, power/performance, kernel, middleware, platform software, or AI deployment on edge/resource-constrained systems.
5+ years of hands-on AI/ML, GenAI, LLM application development, AI platform, or AI-enabled engineering workflow experience preferred.