Forward Deployed Engineer

As an organization, we help enterprises deploy AI systems that are reliable, secure, observable, scalable, and production- ready. As organizations accelerate the adoption of Generative AI, they face growing challenges around reliability, governance, latency, safety, compliance, and operational control. We provide the infrastructure, evaluation frameworks, and real-time guardrails that allow enterprises to confidently operationalize AI at scale. We work closely with enterprises and regulated industries to bridge the gap between AI experimentation and production deployment. Customers include banks, government agencies, insurance companies, and other regulated enterprises, ranging from Fortune 500 companies to top global banks.

About the Role

We’re currently 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. You will work closely with customer engineering teams to integrate our AI platform into complex enterprise ecosystems while helping organizations navigate the operational, governance, and technical challenges of deploying AI at scale. Forward Deployed Engineers act as the technical bridge between customers and our AI's product and engineering teams. You will help customers implement AI guardrails, evaluation systems, observability workflows, and deployment architectures that meet enterprise requirements around reliability, security, compliance, and performance. 

What You'll Own

1. Solve real-world enterprise problems: debug and troubleshoot complex deployment and integration challenges

across customer environments. Navigate ambiguity and adapt solutions to fit real-world operational and regulatory requirements.

1. Deploy and operationalize AI systems: work directly with customer engineering and platform teams to deploy AI's products into enterprise environments. Design and implement integrations across enterprise AI workflows, APIs, infrastructure, and governance systems. 
2. Bridge product, engineering, and customer needs: translate customer deployment challenges into actionable feedback for product and engineering teams. Surface patterns and deployment learnings that improve our AI's platform and implementation playbooks. 
3. Partner closely with customers: work with customer stakeholders across engineering, infrastructure, security, risk, compliance, and operations teams. Help customers navigate enterprise AI governance, evaluation, and approval workflows required for production deployment.

Requirements Must-Have

1. 3-8 years post-undergraduate professional experience (1+ year post-graduation with a master's also acceptable)
2. Strong software engineering experience, especially in distributed systems, Kubernetes, APIs, platform engineering, and enterprise integrations
3. Infra engineer or DevOps background at a startup, big solutions company, or consulting firm. 
4. Traditional DevOps from banks or other "traditional sector" companies is not a fit, according to the hiring manager. 
5. Customer-facing deployment experience: comfortable interfacing with customer engineering, security, and compliance teams
6. Strong scripting fluency. Production code experience not required, but can read, understand, and write clean code.
7. Ability to navigate complex technical and organizational environments independently
8. Comfortable with East Coast US or UK timezones
9. Available for occasional evening calls (twice per week) to collaborate with the India team

Nice-to-Have

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