Senior Forward Deployed AI Engineer

About the role

Build things that run in production. Lead the work that puts a governed, working AI system in front of a customer in weeks. A forward deployed engineer works inside the customer's business. You sit with the people who do the work, learn the process and the data, and build the system that replaces the manual steps. As a senior engineer you are the named engineer on the engagement: the customer's technical point of contact, the person who writes the plan, leads the build, and answers for the result after launch.

Responsibilities

• Work backwards with the customer: write the announcement, the success metrics, and the hard questions before the first line of code
• Design and build production AI systems on the customer's real data: retrieval, multi-step agents, workflow automation, integrations, and the code that makes them dependable
• Choose the right model for each step, from any provider, and keep the system model-neutral so a model swap is a configuration change
• Write evaluation tests before launch and design the human verification and audit trail for every output where an error costs money
• Demo working software to the customer every week and change course on what you learn
• Operate what you ship under the operating agreement: monitoring, incident response, runbooks, and a monthly results review
• Mentor engineers and set the technical bar on your engagement

Requirements

• Eight or more years building and running production software, including hands-on delivery of LLM-based systems
• Strong Python or TypeScript, and sound judgment on data models, APIs, and cloud architecture
• Experience integrating with enterprise systems such as CRM, ERP, ticketing, identity, or a data warehouse
• Comfort working directly with customer teams, executives included, and saying plainly what will and will not work
• A record of owning a system after launch, not only building it
• The habit of telling the customer what you know, what you do not know, and what it will cost

Skills

• Experience with agent frameworks, fine-tuning, or evaluation tooling
• Work in finance, professional services, or the public sector, where audit requirements are real
• Experience with AWS, Azure, or GCP at the platform level