About the Company

Syndesus is recruiting on behalf of a venture-backed (Series A) AI infrastructure startup founded in 2021, with a small but fast-growing team of roughly 11–50 people.

The company helps enterprises move generative AI from experimentation into reliable, secure, production-ready deployment. As organizations adopt GenAI at scale, they run into hard problems around reliability, governance, latency, safety, compliance, and operational control. Our client builds the infrastructure, evaluation frameworks, and real-time guardrails that let enterprises operationalize AI with confidence.

The company works closely with regulated industries — banks, government agencies, insurance companies, and other regulated enterprises ranging from Fortune 500 companies to top global banks.

(The hiring company is not named publicly. Syndesus will share full details with shortlisted candidates.)

About the Role

We're hiring a Forward Deployed Engineer to work directly with enterprise customers to deploy, integrate, and operationalize AI systems in real-world production environments. This role sits at the intersection of engineering, customer deployment, and AI reliability.

This is not a traditional DevOps or pure coding role. It's about execution and ownership — moving things forward, landing solutions inside complex customer environments, and figuring out the integration path end to end. You'll act as the technical bridge between customers and the company's product and engineering teams.

You'd be the third Forward Deployed Engineer globally, joining colleagues in Japan and on the West Coast of the US. This hire covers the East Coast of the US or Europe and works with 3–4 customers concurrently.

How the work breaks down

• ~75% customer-facing: building customer solutions, designing integrations, and making deployments successful inside their environments.
• ~25% internal infrastructure work, similar in flavor to DevOps.

Work is almost entirely remote, typically via secure remote access (e.g. VDI) into customer environments. On-site travel is rare — roughly once or twice a year at most.

What You'll Own

• Solve real-world enterprise problems: debug and troubleshoot complex deployment and integration challenges across customer environments; navigate ambiguity and adapt solutions to real operational and regulatory requirements.
• Deploy and operationalize AI systems: work directly with customer engineering and platform teams to deploy the company's products into enterprise environments. Design and implement integrations across enterprise AI workflows, APIs, infrastructure, and governance systems.
• Bridge product, engineering, and customer needs: translate customer deployment challenges into actionable feedback for the product and engineering teams; surface patterns and learnings that improve the platform and implementation playbooks.
• Partner closely with customers: work with stakeholders across engineering, infrastructure, security, risk, compliance, and operations. Help customers navigate enterprise AI governance, evaluation, and approval workflows required for production deployment.

Requirements

Must-Have

• 3–8 years of post-undergraduate professional experience (1+ year post-graduation with a master's is also acceptable — experience is counted from graduation).
• Strong software engineering background, especially in distributed systems, Kubernetes, APIs, platform engineering, and enterprise integrations.
• Infra or DevOps background at a startup, big solutions company, or consulting firm. Note: traditional DevOps experience confined to banks or other "traditional sector" companies tends not to be a fit — the scope is generally too narrow for this role.
• Customer-facing deployment experience; comfortable interfacing with customer engineering, security, and compliance teams.
• Strong scripting fluency. You don't need to write production software, but you should be able to read, understand, and write clean, simple code. Solid architecture knowledge is essential.
• Ability to navigate complex technical and organizational environments independently — accountable, responsible, and highly motivated.
• Comfortable working East Coast US or UK time zones.
• Available for occasional evening calls (roughly twice per week) to collaborate with the India team.

Nice-to-Have

• Master's or beyond in Computer Science, Engineering, or a related field.
• Familiarity with generative AI systems, LLM applications, AI infrastructure, or model deployment workflows.
• Experience in financial services, healthcare, government, or other regulated industries.
• Familiarity with enterprise security, governance, compliance, or risk-management workflows.
• Experience with AI evaluation, guardrails, observability, or monitoring systems.
• Prior FDE, Solutions Engineering, or Implementation Engineering at a high-growth AI infrastructure or AI tooling startup.

Culture & Logistics

• Fully remote, with a preference for candidates based on the US East Coast (New York is a plus, where the company has an office) or in the UK / Europe.
• Startup environment with flexible hours. Expect a typical five-day work week, with a couple of evenings each week shifted to overlap with the India team. Mornings can also be used for that overlap — it's flexible overall.

Interview Process

1. Initial screen (introductory, non-technical) — ~30 minutes
2. Technical round
3. Mock customer deployment round
4. Domain expertise + behavioral round
5. Founder round
6. Offer