Sr. AI Engineer

Requisition  Si Time Si Time  is the Precision Timing company.

Timing is the heartbeat of all electronics, ensuring performance, resilience and scalability. For decades, quartz devices, non-silicon technology, have kept systems in sync, but they struggle in harsher, more demanding environments. MEMS-based Precision Timing delivers greater accuracy, smaller size and resilience. Today, MEMS timing powers over 400 applications, including high-growth ones in AI datacenters, automated driving, industrial and humanoid robots, wearables and IoT.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 4 billion devices shipped, SiTime is changing the timing industry. For more information, visit: .

Job Summary Si Time  is seeking a hands-on Sr. AI Engineer to design, build, deploy, and operate production-grade AI applications and agents across the enterprise. This role will translate high-value business problems into secure, scalable solutions that connect large language models with SiTime data, knowledge, and core systems. The engineer will work across the full solution lifecycle, from rapid prototyping through evaluation, deployment, monitoring, and continuous improvement, with a strong focus on measurable business value, responsible AI, and reusable engineering patterns.

It is not necessary to meet all job requirements to be a qualified candidate for the position. We value diverse experiences, perspectives, and career paths, and welcome candidates who are excited to contribute to our mission.

Responsibilities Build production AI applications. Design and implement LLM-powered applications and agentic workflows that move from prototype to reliable production use.

Develop enterprise agents and integrations. Connect AI solutions securely to business systems, data platforms, APIs, knowledge bases, and workflow tools using approved integration patterns.

Implement retrieval and knowledge solutions. Build retrieval-augmented generation (RAG) pipelines, including ingestion, chunking, indexing, metadata, access controls, retrieval, and grounding.

Engineer for quality and trust. Create evaluation datasets and automated tests for accuracy, completeness, safety, latency, reliability, and cost; add human review where appropriate.

Own deployment and operations. Establish CI/CD, observability, prompt and model versioning, incident response, and ongoing performance and cost monitoring for AI services.

Build reusable platform components. Develop shared services and patterns for model access, orchestration, secure connectors, memory, tool use, auditability, and policy enforcement.

Apply responsible AI and security controls. Protect confidential information and intellectual property through least privilege access, data handling controls, logging, red-teaming, and adherence to SiTime policies.

Partner with business and technology teams. Work with product owners, business functions, Business Applications, Data, Security, and IT to define outcomes, prioritize use cases, and deliver solutions that fit existing workflows.

Measure business value. Instrument adoption and outcome metrics, quantify time, quality, or cycle-time improvements, and iterate based on evidence.

Advance engineering practices. Conduct design and code reviews, document architectures and runbooks, mentor engineers, and stay current on models, frameworks, and enterprise AI patterns.

Qualifications & Requirements Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; a master's degree is preferred.
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,…