- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Education
- Master
- Source
- RecruiterFlow
Description
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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.
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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.
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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.
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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.
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3-8 years post-undergraduate professional experience (1+ year post-graduation with a master's also acceptable)
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Strong software engineering experience, especially in distributed systems, Kubernetes, APIs, platform engineering, and enterprise integrations
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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.
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Customer-facing deployment experience: comfortable interfacing with customer engineering, security, and compliance teams
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Strong scripting fluency. Production code experience not required, but can read, understand, and write clean code.
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Ability to navigate complex technical and organizational environments independently
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Comfortable with East Coast US or UK timezones
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Available for occasional evening calls to collaborate with the India team
Nice-to-Haves:
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Master's or beyond in Computer Science, Engineering, or related field
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Familiarity with Generative AI systems, LLM applications, AI infrastructure, or model deployment workflows
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Experience working in financial services, healthcare, government, or other regulated industries
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Familiarity with enterprise security, governance, compliance, or risk management workflows
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Experience with AI evaluation, guardrails, observability, or monitoring systems
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Prior FDE, Solutions Engineering, or Implementation Engineering at a high-growth AI infrastructure or AI tooling startup
