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.
Deploy and operationalize AI systems: work directly with customer engineering and platform teams to deploy 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 product and engineering teams. Surface patterns and deployment learnings that improve the platform and implementation playbooks.
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.
3-8 years post-undergraduate professional experience (1+ year post-graduation with a master's also acceptable)
Strong software engineering experience, especially in distributed systems, Kubernetes, APIs, platform engineering, and enterprise integrations
Infra engineer or DevOps background at a startup, big solutions company, or consulting firm. Traditional DevOps from banks or other "traditional sector" companies is not a fit, according to the hiring manager.
Customer-facing deployment experience: comfortable interfacing with customer engineering, security, and compliance teams
Strong scripting fluency. Production code experience not required, but can read, understand, and write clean code.
Ability to navigate complex technical and organizational environments independently
Comfortable with East Coast US or UK timezones
Available for occasional evening calls to collaborate with the India team
Master's or beyond in Computer Science, Engineering, or related field
Familiarity with Generative AI systems, LLM applications, AI infrastructure, or model deployment workflows
Experience working 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