About this role
AI Forward Deployed Engineer
Full time
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New York, New York | Hybrid
5 - 12 Years of Experience
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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.
AI Forward Deployed Engineer
Location
New York, NY
Hybrid, with occasional on-site customer visits approximately 1–2 times per month.
Company Stage of Funding
Series A
Total Funding: $37M
Office Type
Hybrid. Engineers work from the New York City office and occasionally visit customer sites for technical workshops and deployment-related work.
Salary
$180,000–$300,000 base salary
Equity
Competitive equity compensation.
Visa Not open to visa sponsorship or transfers. Candidates must already have unrestricted US work authorization, such as US citizens or Green Card holders.
Employment Type
Full-time
Company Description
This company is building an AI-powered autonomous supply chain platform that automates complex procurement workflows for mid-market and upper mid-market businesses with physical supply chains.
Its platform uses AI agents to automate the procurement lifecycle, including sourcing suppliers, gathering quotes, submitting purchase orders, and following up with vendors on deliveries and payments. The goal is to eliminate repetitive manual work, improve operational efficiency, and help procurement teams respond to changing market conditions, supplier disruptions, tariffs, and geopolitical risks.
The company has raised approximately $37M in total funding, including a $30M Series A in February 2026 backed by prominent venture capital firms and strategic investors. Founded in 2023 and headquartered in New York City, the company is growing rapidly following its Series A.
The engineering organization is lean, generalist-oriented, and focused on delivering direct customer impact. Forward Deployed Engineers work closely with enterprise customers to understand complex procurement and ERP workflows, then design and implement integrations, backend services, and AI-powered automations tailored to those needs.
The engineering culture emphasizes customer-first development, end-to-end ownership, first-principles problem-solving, and rapid execution. Engineers are expected to own their solutions from requirements gathering through implementation, deployment, and ongoing improvement, with opportunities to turn customer-specific solutions into reusable platform capabilities.
The team values engineers who are comfortable working across backend, frontend, infrastructure, and AI-powered workflows rather than operating within narrow technical boundaries.
What You Will Do
- Understand Customer Workflows and Requirements
Work directly with enterprise customers to understand end-to-end procurement, ERP, and operational workflows.
Translate ambiguous business requirements into practical, scalable technical solutions.
Identify inefficiencies in existing customer processes and determine how software and AI agents can automate them.
Gather technical requirements and validate assumptions before designing and implementing solutions.
Participate in customer discovery sessions, deployment planning, and occasional on-site technical workshops.
- Build Enterprise Integrations
Design and implement integrations with enterprise systems such as NetSuite, SAP, and Microsoft Business Central.
Build API integrations, data pipelines, and backend services that connect customer systems to the platform.
Handle complex data flows, system interoperability, and customer-specific integration requirements.
Ensure integrations are reliable, maintainable, and capable of supporting production workloads.
Troubleshoot integration issues across external systems and internal services.
- Develop Backend Services and Workflow Automations
Build backend services, APIs, and workflow automation systems using Python, Django, or comparable backend technologies.
Develop solutions that automate procurement activities, including supplier sourcing, quote collection, purchase order creation, and vendor follow-ups.
Implement asynchronous processing and background workflows using technologies such as Celery, Redis, and RabbitMQ.
Build customer-specific functionality while maintaining a focus on reliability, maintainability, and future scalability.
Own implementation from initial design through deployment and production support.
- Build and Deploy AI-Powered Workflows
Help bring AI agents into production by connecting them with enterprise systems and customer workflows.
Develop integrations and supporting backend infrastructure that allow AI agents to execute complex operational tasks.
Identify opportunities to automate manual work through AI-driven workflows and intelligent system orchestration.
Work with modern AI coding tools and incorporate AI-native development practices into daily engineering work.
Focus on practical AI applications that deliver measurable customer and business outcomes.
- Own Solutions End to End
Take responsibility for technical solutions across architecture, implementation, testing, deployment, and maintenance.
Work across backend, frontend, and infrastructure when needed rather than relying on rigid engineering specializations.
Deploy and operate services using containers and cloud infrastructure.
Make thoughtful decisions around technical trade-offs, speed, reliability, and long-term maintainability.
Troubleshoot production issues and ensure customer-specific implementations work effectively in real-world environments.
- Collaborate With Product and Engineering
Partner with Product and Engineering to identify repeatable patterns across customer deployments.
Convert successful customer-specific implementations into reusable platform capabilities where appropriate.
Share customer feedback and technical insights that can inform the core product roadmap.
Balance immediate customer needs with the long-term evolution of the platform.
Contribute to architectural decisions and help improve engineering practices as the team scales.
Ideal Candidate Background
Experience Requirements
5+ years of professional software engineering experience, with strong backend engineering fundamentals.
Demonstrated experience building and maintaining production backend systems.
Experience translating business or customer requirements into technical implementations.
Hands-on experience building integrations with external APIs, enterprise software, or complex data pipelines.
Evidence of owning projects end to end, from requirements gathering and design through implementation and deployment.
Comfortable working in ambiguous, fast-moving environments where engineers are expected to make decisions independently.
Strong customer orientation and an interest in solving real operational problems through software.
Technical Requirements
Strong proficiency in Python and/or Django, or comparable backend technologies.
Experience building backend services, APIs, integrations, and workflow automation systems.
Experience integrating with external APIs, enterprise applications, or third-party platforms.
Ability to reason about data flows, system boundaries, failure handling, and integration reliability.
Familiarity with databases, asynchronous processing, background jobs, and distributed application components.
Experience deploying and operating production software.
Strong system design and architectural judgment, with the ability to build solutions that are reliable and maintainable.
Enterprise Integrations and Systems Thinking
Experience connecting multiple systems through APIs, data pipelines, or integration frameworks.
Ability to understand complex operational workflows and translate them into software requirements.
Comfortable debugging issues that span internal services and external platforms.
Able to challenge assumptions, ask clarifying questions, and identify the simplest effective solution.
Understands how to design integrations that accommodate different customer environments and business requirements.
AI and Automation
Interested in building practical AI-powered products and bringing AI capabilities into production.
Able to identify manual workflows that can be automated through software and AI agents.
Comfortable working with AI-assisted engineering tools and adapting to evolving development practices.
Understands that production AI systems require reliable integrations, thoughtful workflow design, and appropriate error handling.
Experience with AI agents, LLM-powered applications, or agentic workflows is a strong a