Forward Deployed Engineer, AI Enablement

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms. Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe, and is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.

About the role

As a Senior Forward Deployed Engineer on the AI Enablement team, your job is to get close to the people doing the work, understand exactly where the friction lives, and build agentic AI systems that make it disappear permanently. This role is not a traditional product engineering position. You will operate more like an embedded problem-solver than a member of a feature team — pairing engineering depth with the curiosity and judgment to identify what is actually worth building. Your users are Stord employees, and you will receive direct, immediate feedback on the impact of what you build.

This is a greenfield opportunity: we are early in systematically automating internal workflows with AI, and you will define how it's done. You will have high autonomy to observe a problem, propose a solution, and ship it with minimal bureaucratic drag. Your work compounds as reusable modules and patterns you build accelerate every automation that follows. AI investment is a company-level mandate, so you will have organizational support and visibility.

Responsibilities

• Embed with internal teams (CX, Finance, Ops, Logistics), map workflows end-to-end, and identify the highest-leverage automation targets.
• Build AI-powered tooling for Customer Experience teams — triaging issues, surfacing context, automating resolution workflows.
• Develop finance automation for reconciliation, exception handling, and reporting workflows that currently require manual effort.
• Create ops and logistics tooling — dashboards, alerting, and intelligent interfaces that reduce the manual burden on warehouse and transportation teams.
• Design LLM-powered interfaces that let non-technical teams query, act on, and get answers from operational data without needing engineering support.
• Build end-to-end automation pipelines: observe workflow → prototype → validate with domain experts → harden → ship to production.
• Develop agentic systems with proper logging, retries, monitoring, and edge case handling — not scripts that break silently.
• Integrate with internal systems (OMS, WMS, TMS, Billing) via APIs and event streams.
• Create reusable automation modules that accelerate future workflow projects across the organization.
• Build internal AI-powered tools that reduce friction across the engineering development lifecycle.
• Develop lightweight APIs and integrations that connect the systems engineers rely on daily.
• Create tooling that helps engineers at Stord write, review, and ship code faster using agentic workflows.
• Automate reporting and alerting that replaces manual data pulling and analysis.
• Build dashboards and data products that surface operational intelligence to the teams who need it.
• Develop self-serve data interfaces that reduce the back-and-forth between operational teams and engineering.
• Ship small, validate with domain experts, and iterate fast — maintaining production discipline from the very first commit.
• Collaborate with Product Engineering when automations touch core platform systems; operate independently everywhere else.
• Document and publish reusable patterns so the next engineer — or the next automation — goes faster.

Requirements

• TypeScript / Node.js (3+ years): Production backend experience. You reach for the right tool, and this is your primary one.
• Agentic AI development: You have built AI agents that automate real workflows in production — not toys, not demos.
• CLI-native workflow: Claude Code, Cursor, Codex, or equivalent is your primary development environment. This is a hard requirement.
• LLM integration: Proven experience with OpenAI, Anthropic, or equivalent — tool use, structured outputs, prompt engineering, error handling.
• API design & integration: You have built RESTful APIs from scratch and integrated with complex internal systems.
• Observability: You instrument your agents in production — logging, tracing, monitoring, alerting. You know when things break before users do.
• Database: Advanced SQL with PostgreSQL. You can model data and write queries that matter.
• High agency: You identify the problem worth solving, propose the approach, and drive to done with minimal direction.
• Production discipline: Fast iteration does not mean fragile systems. You build things that stay running.

Qualifications

• Ownership & Accountability: You own features end-to-end and take pride in what you ship. You follow through from design to production and don't drop things.
• Strong Communication: You can explain technical decisions and trade-offs to engineers, PMs, and stakeholders. You ask good questions and listen well.
• Collaborative Approach: You work well with others, give constructive code review feedback, and actively seek input from teammates.
• Production Mindset: You prioritize reliability and user impact. You think about failure modes, monitoring, and operational concerns as part of your design process.
• Learning Agility: You're comfortable with rapidly evolving AI/ML technologies and tools. You stay current without chasing hype.
• Directed AI-Assisted Development: You know how to use AI coding tools as a productivity multiplier while maintaining quality and your own technical judgment.

Skills

Strongly Preferred:

• Experience automating workflows in operational or back-office contexts (finance, support, logistics, HR).
• Familiarity with Stord's stack: Elixir/Phoenix, TypeScript, Kafka, GCP.
• Vector databases and semantic search for internal knowledge retrieval.
• Experience building internal developer tools or platforms.
• Python for scripting, data wrangling, or model integration.

Nice to Have:

• Early-stage startup background — you have worn many hats and shipped under pressure.
• Hackathon experience or open source contributions.
• Domain knowledge in logistics, supply chain, or operations-heavy B2B environments.
• Experience with workflow orchestration tools (Temporal, Prefect, Airflow, or similar).

What Success Looks Like

• First 30 days: You have embedded with at least one internal team, mapped a workflow end-to-end, and shipped an automation — however small — into production.
• 90 days: You have eliminated a meaningful chunk of manual work that was previously just accepted as the cost of doing business.
• Six months: You have a library of reusable patterns others are building on, and internal teams are coming to you with problems rather than waiting to be found.