[Hiring] Forward Deployed Engineer @Recruiting From Scratch

Role Description

As a Forward Deployed Engineer, you'll own the technical lifecycle of enterprise customers — from initial architecture and on-premises deployment through configuration, ongoing maintenance, integrations, and expansion. This is an opportunity to combine hands-on software engineering with enterprise technical engagement while working on complex AI infrastructure and governance problems.

What You'll Do

• Own enterprise on-premises and cloud deployments from architecture through production rollout
• Work directly with enterprise engineering teams to understand infrastructure requirements and deployment constraints
• Stand up and configure production deployments across customer environments
• Configure SAML/SSO, SCIM, cloud environments, networking, and enterprise integrations
• Deploy and maintain production systems using Kubernetes, Docker, Helm, and cloud infrastructure
• Own customer environments after deployment and ensure ongoing reliability
• Diagnose and resolve complex integration, infrastructure, and deployment issues
• Write production code in Python and/or TypeScript to unblock customer environments
• Develop bug fixes, configuration changes, and small product features autonomously
• Work directly in terminal environments and use shell/bash scripting to troubleshoot systems
• Help enterprise customers onboard AI use cases into the platform
• Understand customer LLM pipelines, RAG systems, and agent workflows
• Implement AI evaluation and monitoring capabilities that fit customer technology stacks
• Integrate customer AI systems with APIs, SDKs, LLM gateways, and observability infrastructure
• Act as a trusted technical partner to enterprise engineering teams
• Build strong technical relationships with customers and help drive adoption and expansion
• Troubleshoot complex production environments across cloud, networking, Kubernetes, and AI infrastructure
• Turn customer learnings into reusable deployment playbooks and reference architectures
• Create integration templates and technical documentation that improve future deployments
• Collaborate closely with core engineering and product teams to communicate recurring customer needs
• Identify opportunities to productize repeated deployment and integration challenges
• Operate with high autonomy in a fast-moving enterprise AI environment

Qualifications

• 3–6 years of professional software engineering, solutions engineering, forward deployed engineering, or customer-facing technical experience
• Explicit experience working as a Forward Deployed Engineer, Solutions Engineer, Customer Success Engineer, or similar technical customer-facing role
• Experience working at a B2B SaaS, AI/ML, enterprise software, or infrastructure company
• Experience owning enterprise technical deployments
• Experience working directly with enterprise engineering or infrastructure teams
• Experience supporting production customer environments
• Experience troubleshooting complex enterprise infrastructure
• Experience deploying and maintaining software in customer environments
• Experience working with enterprise authentication and cloud environments
• Experience shipping production code to solve customer problems
• Experience operating independently in ambiguous technical environments
• Strong customer-facing communication and relationship-building skills
• Experience translating customer technical requirements into working solutions

Technical Requirements

• Strong Kubernetes experience
• Strong Docker and containerization experience
• Hands-on experience with Helm charts
• Experience with at least one major cloud platform: AWS, Azure, or GCP
• Comfortable working in Linux terminal environments
• Strong bash/shell scripting experience
• Production Python and/or TypeScript experience
• Experience with enterprise APIs and SDKs
• Experience configuring SAML/SSO
• Experience with SCIM and enterprise identity integrations
• Experience with CI/CD pipelines and deployment automation
• Experience with VPCs, networking, and cloud infrastructure
• Strong debugging and production troubleshooting skills
• Experience working with distributed systems and production infrastructure
• Familiarity with OpenTelemetry or observability tooling preferred
• Familiarity with LLM gateways and AI infrastructure preferred
• Experience working with LLM, RAG, or agentic AI systems preferred
• Strong understanding of production reliability and system operations

Education

• Bachelor's degree or higher in Computer Science, Engineering, or related technical field preferred
• Strong software engineering and systems fundamentals
• Equivalent practical engineering experience accepted

Soft Skills

• Exceptional customer-facing communication
• Strong technical credibility with engineering audiences
• Patient and empathetic communication style
• Thick-skinned and comfortable handling difficult technical situations
• Strong ownership mentality
• High execution velocity
• Excellent problem-solving ability
• Comfortable operating in ambiguity
• Strong debugging instincts
• Comfortable working independently
• Strong customer obsession
• Ability to explain complex infrastructure concepts clearly
• Comfortable working directly with senior engineers and technical leaders
• Bias toward action
• Low-ego collaborative mentality
• Strong attention to technical detail
• Ability to balance customer needs with scalable product solutions
• Comfortable working across engineering, infrastructure, and customer-facing responsibilities

Compensation & Benefits

• Base Salary: $170,000 – $240,000
• Competitive Equity: 0.2% – 0.3%
• Fully Remote Work Environment
• Open to OPT and H1B Transfers
• Opportunity to work directly with Fortune 500 and Fortune 2000 customers
• Significant ownership over enterprise deployments
• Direct exposure to complex AI infrastructure environments
• Opportunity to work across Kubernetes, cloud infrastructure, AI observability, and enterprise security
• High-growth Series A environment
• Direct collaboration with technical founders and engineering leadership
• Opportunity to turn customer problems into reusable product capabilities
• High-impact role at the intersection of engineering and enterprise AI