Head of AI Applications

SiTime Corporation•Santa Clara, CA

About The Position

About SiTime SiTime Corporation is the precision timing company. Our semiconductor MEMS programmable solutions offer a rich feature set that enables customers to differentiate their products with higher performance, smaller size, lower power and better reliability. With more than 3 billion devices shipped, SiTime is changing the timing industry. For more information, visit www.sitime.com. Job Summary We are seeking a Head of AI Applications to define and execute the strategy for AI-powered solutions across SiTime. This role will lead the development, deployment, and scaling of AI applications that drive measurable business outcomes across functions including Sales, Finance, Operations, HR, and Marketing. This is a hands-on leadership role responsible for building a high-performing team, delivering production-grade AI solutions, and driving company-wide adoption of AI as a trusted, everyday capability. It is not necessary to meet all job requirements to be a qualified candidate for the position.

Requirements

5+ years of experience building software or AI solutions, with 4+ years in AI/ML and 2+ years leading applied-AI teams.
A track record of building agentic AI solutions for business functions (e.g., Sales, Finance, Operations, HR) and shipping them to production at scale — not just prototypes.
Hands-on experience with the Microsoft Copilot ecosystem — M365 Copilot, Copilot Studio, and agent builder — to build, customize, and deploy agents across the organization.
Demonstrated success driving AI adoption and enablement across an organization: change management, training, and measurable uptake.
Experience architecting full-stack AI solutions — selecting the right LLMs for each use case and integrating with core business systems (CRM, ERP, data platforms, etc.).
Deep, current understanding of LLMs and generative AI, including prompting, retrieval-augmented generation (RAG), fine-tuning, agents, and evaluation methods.
Strong engineering fundamentals: you can go deep with your team on architecture, integrations, and trade-offs.
Proven ability to hire, lead, and develop high-performing technical teams.
Excellent communication and influence skills; you can rally non-technical teams around AI and explain complex concepts to executives and end users alike.
A practical grasp of responsible AI — safety, privacy, bias, and governance.
Experience establishing AI governance frameworks in an enterprise — policies, review and approval processes, and operating models (e.g., an AI council or center of excellence).
Experience hiring, managing, and developing direct reports; proven ability to build and grow high-performing AI teams.
Experience presenting AI strategy, progress, and recommendations to senior leadership and C-suite stakeholders.
Hands-on experience with agentic AI orchestration frameworks (e.g., LangChain, Semantic Kernel, AutoGen, or similar) for designing and deploying multi-step agent workflows.
Familiarity with AI governance and compliance frameworks such as the NIST AI Risk Management Framework (AI RMF) or EU AI Act, applied in an enterprise context.
Demonstrated change management experience — driving adoption of new technology or ways of working across a broad, cross-functional organization.
Experience managing vendor and partner relationships for AI tooling, platforms, or services, including contract evaluation and strategic alignment.

Nice To Haves

Strategic and hands-on. Comfortable setting vision at the leadership table and rolling up your sleeves to build alongside your team.
Influences without authority. Wins buy-in across functions and seniority levels through credibility, clarity, and trust rather than mandate.
Business-first mindset. Starts from the problem and the outcome, not the technology — relentlessly focused on measurable value.
Bias for action. Moves fast, ships pragmatically, and iterates; favors progress over perfection in a fast-moving field.
Comfortable with ambiguity. Thrives amid rapid change and incomplete information, and brings structure to the unknown.
Change agent and evangelist. Energizes others about AI, builds momentum, and makes adoption feel achievable rather than threatening.
Strong communicator and translator. Bridges the gap between technical teams and business stakeholders effortlessly.
Intellectually curious. A continuous learner who stays ahead of an evolving landscape and brings the best ideas back to the organization.
High integrity and judgment. Champions responsible, trustworthy AI and earns the confidence of leadership and end users alike.

Responsibilities

Set the AI applications strategy. Define and own the vision and roadmap for AI-powered solutions, aligned to company and business-function priorities.
Build agentic AI across the business. Design and deliver agentic AI solutions for functions across the organization — Sales, Finance, Operations, HR, Marketing, and more — that automate workflows and augment teams.
Drive AI adoption and enablement. Champion AI across the company: lead change management, training, and enablement so teams actually use AI in their day-to-day, and measure and grow adoption over time.
Own the full-stack AI solution. Architect end-to-end solutions with the right LLMs for each use case, and integrate them with core business systems and data sources (CRM, ERP, ticketing, knowledge bases, etc.).
Ship production AI. Lead the design, development, evaluation, and deployment of LLM- and agent-driven applications — from prototype to scaled production.
Partner cross-functionally. Work closely with business leaders, IT, Product, and Engineering to identify high-impact opportunities and turn them into shipped value.
Partner with Business Applications. Collaborate closely with the Business Applications team to embed AI directly into core enterprise systems and workflows, ensuring solutions are integrated, supportable, and scalable.
Own evaluation and quality. Establish rigorous evaluation, testing, and monitoring practices for model performance, accuracy, latency, and cost.
Manage the model and platform stack. Make build-vs-buy decisions across foundation models, fine-tuning, RAG, agents, and orchestration; balance capability, cost, and reliability.
Stand up AI governance. Establish SiTime's AI governance framework — policies, review processes, an operating model (e.g., an AI council or center of excellence), and clear standards for how AI is evaluated, approved, and deployed.
Drive responsible AI. Set standards and guardrails for safety, privacy, fairness, and governance, and ensure compliance with relevant policies and regulations.
Be the internal expert. Keep the organization current on the fast-moving AI landscape and advise leadership on where to invest.