Sr. AI Engineer

SiTime Corporation•Santa Clara, CA

5d•$124,920 - $171,770

About The Position

SiTime is seeking a hands-on Sr. AI Engineer to design, build, deploy, and operate production-grade AI applications and agents across the enterprise. The role translates high-value business problems into secure, scalable solutions that connect large language models with SiTime data, knowledge, and core systems. It spans the full solution lifecycle, from prototyping through evaluation, deployment, monitoring, and continuous improvement, with emphasis on measurable business value, responsible AI, and reusable engineering patterns. The engineer partners with product owners and teams across business functions, Business Applications, Data, Security, and IT to prioritize use cases and deliver AI solutions that fit existing workflows. The position also advances engineering practices through design and code reviews, architecture and runbook documentation, mentoring, and staying current with models, frameworks, and enterprise AI patterns.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
7+ years of software engineering experience, including 3+ years building AI/ML or generative AI solutions.
Demonstrated experience shipping LLM-based applications or agents to production, not only prototypes.
Strong Python skills and experience building production services, APIs, and distributed or cloud-native applications.
Working knowledge of LLM application patterns, including prompting, RAG, tool/function calling, structured outputs, agent orchestration, and model evaluation.
Experience with vector search, embeddings, document processing, data pipelines, and enterprise knowledge retrieval.
Experience with cloud AI services and modern engineering practices, including containers, CI/CD, infrastructure automation, testing, monitoring, and source control.
Understanding of identity, authorization, secrets management, data privacy, secure API design, and responsible AI controls.
Ability to integrate with enterprise platforms such as ERP, CRM, collaboration, ticketing, and data systems.
Excellent written and verbal communication skills, including ability to translate business needs into technical designs and explain trade-offs clearly.
English proficiency sufficient to communicate, collaborate, and perform job responsibilities in a professional business environment.

Nice To Haves

Master's degree.
Hands-on experience with the Microsoft AI ecosystem, such as Azure AI Foundry, Azure OpenAI, Microsoft 365 Copilot, GitHub Copilot, Copilot Studio, Microsoft Graph, and Power Platform.
Experience with one or more orchestration frameworks and model providers, while maintaining provider-independent architecture where practical.
Experience building governed enterprise agent marketplaces, reusable agent components, or model gateways.
Experience with MLOps/LLMOps, automated evaluation, red-teaming, tracing, and AI cost management.
Experience applying AI to enterprise workflows in Sales, Finance, Operations, HR, Engineering, or Customer Support.
Semiconductor, electronics, or manufacturing industry experience.

Responsibilities

Design and implement LLM-powered applications and agentic workflows, taking them from prototype to reliable production use.
Connect AI solutions securely to business systems, data platforms, APIs, knowledge bases, and workflow tools using approved integration patterns.
Build retrieval-augmented generation (RAG) pipelines, including ingestion, chunking, indexing, metadata, access controls, retrieval, and grounding.
Create evaluation datasets and automated tests for accuracy, completeness, safety, latency, reliability, and cost; include human review where appropriate.
Establish CI/CD, observability, prompt and model versioning, incident response, and ongoing performance and cost monitoring for AI services.
Develop shared services and reusable patterns for model access, orchestration, secure connectors, memory, tool use, auditability, and policy enforcement.
Protect confidential information and intellectual property through least-privilege access, data-handling controls, logging, red-teaming, and adherence to SiTime policies.
Partner with product owners and teams across business functions, Business Applications, Data, Security, and IT to define outcomes, prioritize use cases, and deliver solutions suited to existing workflows.
Instrument adoption and outcome metrics, quantify time, quality, or cycle-time improvements, and iterate based on evidence.
Conduct design and code reviews, document architectures and runbooks, mentor engineers, and stay current on models, frameworks, and enterprise AI patterns.