Forward Deployed Engineer

Who is Recruiting from Scratch:

Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.

Forward Deployed Engineer
Location - United States (Remote) - Fully remote role. Candidates should be US-based and able to work US hours. Western Europe/India may be considered for timezone coverage.
Compensation - $170,000 - $240,000 Base + 0.2% - 0.3% Competitive Equity
Visa - Open to Visa Transfers (OPT, H1B Transfers)
Company Stage - Series A - $19.3M Total Funding
Industry - Artificial Intelligence, AI Governance, AI Observability, B2B SaaS, Enterprise Software, Data Infrastructure

About the Company

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.

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

Ideal Candidate Background
Experience 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

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

Why Join

This is an opportunity to join a high-growth AI infrastructure company helping some of the world's largest enterprises safely deploy and manage AI systems.

You'll work directly with sophisticated engineering organizations, solving real-world deployment and infrastructure challenges across Kubernetes, cloud environments, enterprise identity, AI pipelines, RAG systems, and agentic workflows.

As a Forward Deployed Engineer, you'll have unusual breadth: you'll write production code, deploy systems, debug infrastructure, work directly with customers, and influence how the product evolves based on what you learn in the field.

If you enjoy being hands-on with infrastructure, solving messy enterprise problems, working directly with technical customers, and turning complex deployment challenges into scalable solutions, this role offers exceptional technical ownership and impact.