Sr. Engineer, AI Applications

Ayar Labs•San Jose, CA

5d•Onsite

About The Position

Ayar Labs is shattering AI data bottlenecks by moving data at the speed of light. As pioneers of co-packaged optics (CPO), we are using light instead of electricity to move data faster, further, and with a fraction of the energy needed to fuel the explosive growth of AI models. Backed by industry giants like NVIDIA, AMD, Mediatek and Intel and manufactured in partnership with the world’s leading semiconductor ecosystem, Ayar Labs’ co-packaged optics solution is key to unleashing next-generation AI scale-up architectures. This AI Application Engineer is a builder who turns AI into tools our Ayar Labs employees actually use. You will design, build, and deploy tools that put AI directly into the hands of engineering, operations, and business teams who know their own work, but aren't software or AI specialists. This is a self-motivated individual contributor role for someone with hands-on experience who can take a vague request and turn it into a working, reliable tool. You will partner closely with stakeholders across the business, and with the rest of IT, to build AI-powered tools that will actually move the needle. Beyond building, you will help shape the standards, processes, and infrastructure this new AI function uses to grow, leveraging modern DevOps tooling alongside LLM and AI APIs to ship solutions that hold up in production.

Requirements

Bachelor's degree in computer science, software engineering, electrical engineering, computer engineering, mathematics, physics, or another STEM or engineering field.
2+ years in software engineering, DevOps, or platform engineering.
Strong independent projects, internships, or open-source contributions are encouraged to apply.
Hands-on experience working across model APIs — Anthropic, OpenAI, Gemini, and open weight models — building a range of AI-powered tools such as chat and assistant interfaces, document processing and summarization, workflow automation, or code-generation tooling.
Experience building AI skills that can be shared across teams and the organization, and MCP (Model Context Protocol) servers that support technical users.
Sound judgment on when to rely on the tools already built into major frontier model apps and when it's worth building something custom instead.
Strong written and verbal skills, with the ability to translate a request from someone outside software or AI into a working tool.
A history of building internal tools or applications that other people actually use, not coursework or personal experiments alone.
Comfortable operating without an existing playbook, and motivated by the chance to help write one.
Working proficiency in Python, plus at least one of JavaScript/TypeScript, Go, or a similar modern language.
Comfort with the tools that keep applications running in production — version control, CI/CD, containerization and orchestration, infrastructure-as-code, and monitoring.
Cloud platform experience (AWS, Azure, or GCP) is a plus.

Nice To Haves

Thoughtful about data handling, access control, and IP protection when introducing AI tools into a company that guards sensitive engineering and manufacturing data
A master's or other postgraduate degree is preferred.
Experience testing and evaluating AI-powered features, and building in the guardrails that keep them reliable and safe for internal users.
Prior experience at a hardware, semiconductor, or deep-tech company, or another environment with highly technical, non-software users.

Responsibilities

Serve as a primary builder for AI-powered internal tools, turning requests from across the company into scoped, working solutions.
Build the apps, dashboards, services, and interfaces that put AI capabilities directly in front of users, so they can get their work done without needing to understand how the AI works.
Connect tools such as OpenAI, Anthropic, Gemini, or open weight models into real company workflows and data sources, so the AI is doing useful work rather than sitting in a demo.
Build and maintain the pipelines, containerized deployments, infrastructure automation, and monitoring these tools run on, so what you ship keeps working after launch.
Troubleshoot issues directly with users, and drive adoption through documentation and training people will actually use.
Evaluate new AI tools, platforms, and vendors for opportunities which fit our company needs.