Forward Deployed Engineer

Recruiting From Scratch•San Francisco, CA

11d•Remote

About The Position

Our client is building an AI governance and observability platform designed for large enterprises deploying AI systems at scale. The platform provides technical controls that help organizations evaluate, monitor, govern, and safely deploy AI systems across the full AI lifecycle. It supports traditional machine learning models as well as modern LLMs, RAG pipelines, and multi-step agentic workflows. The company primarily serves Fortune 500 and Fortune 2000 organizations across highly regulated industries including financial services, healthcare, and telecommunications. The platform provides automated evaluation capabilities, production guardrails, AI observability, traceability, governance workflows, and flexible SaaS or on-premises deployment options. The team is approximately 25 people and operates with a strong engineering-first, high-ownership culture. Engineers work directly with enterprise customers and the founding team, solving complex infrastructure problems and turning field learnings into reusable product capabilities. 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.

Requirements

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

Nice To Haves

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
Equivalent practical engineering experience accepted

Responsibilities

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

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