Forward Deployed Engineer, AI Enablement

Stord•Remote, United States, GA

116d

About The Position

Stord is seeking an energetic expert to join the AI Enablement team as a Senior Forward Deployed Engineer. This role is focused on building agentic AI systems to automate internal workflows and eliminate manual work for Stord employees. The engineer will work closely with internal teams like Customer Experience, Finance, Operations, and Logistics to understand their challenges and build production-ready tools. This is a unique opportunity to define how Stord automates internal processes with AI, offering high autonomy and the chance to see the direct impact of your work. The role involves building tools for internal teams, end-to-end workflow automation, developer productivity tooling, and internal data/analytics products. The ideal candidate will have a strong background in backend development, agentic AI, LLM integration, and production discipline, with a focus on shipping reliable, impactful solutions.

Requirements

TypeScript / Node.js (3+ years): Production backend experience.
Agentic AI development: Built AI agents that automate real workflows in production.
CLI-native workflow: Experience with Claude Code, Cursor, Codex, or equivalent as a primary development environment.
LLM integration: Proven experience with OpenAI, Anthropic, or equivalent, including tool use, structured outputs, prompt engineering, and error handling.
API design & integration: Experience building RESTful APIs from scratch and integrating with complex internal systems.
Observability: Experience instrumenting agents in production with logging, tracing, monitoring, and alerting.
Database: Advanced SQL with PostgreSQL, including data modeling and query writing.
High agency: Ability to identify problems, propose solutions, and drive to completion with minimal direction.
Production discipline: Ability to build reliable systems that stay running through fast iteration.
Ownership & Accountability: Own features end-to-end from design to production.
Strong Communication: Ability to explain technical decisions to various stakeholders and listen well.
Collaborative Approach: Ability to work well with others, provide constructive feedback, and seek input.
Production Mindset: Prioritize reliability and user impact, considering failure modes and operational concerns.
Learning Agility: Comfortable with rapidly evolving AI/ML technologies and tools.

Nice To Haves

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.
Early-stage startup background.
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).

Responsibilities

Build agentic AI systems that automate internal workflows and eliminate manual work.
Develop AI-powered tooling for Customer Experience teams, including issue triaging, context surfacing, and resolution automation.
Create finance automation tools for reconciliation, exception handling, and reporting.
Build Ops and logistics tooling such as dashboards, alerting, and intelligent interfaces.
Develop LLM-powered interfaces for non-technical teams to query operational data.
Design and implement end-to-end automation pipelines, from workflow observation to production deployment.
Create agentic systems with robust logging, retries, monitoring, and edge case handling.
Integrate with internal systems (OMS, WMS, TMS, Billing) via APIs and event streams.
Develop reusable automation modules to accelerate future workflow projects.
Build internal AI-powered tools to enhance the engineering development lifecycle.
Create lightweight APIs and integrations for systems engineers rely on.
Develop tooling to help engineers write, review, and ship code faster using agentic workflows.
Automate reporting and alerting to replace manual data pulling and analysis.
Build dashboards and data products to surface operational intelligence.
Develop self-serve data interfaces to reduce inter-team dependencies.
Embed with internal teams to map workflows and identify automation targets.
Iterate quickly on solutions, validate with domain experts, and maintain production discipline.
Collaborate with Product Engineering on automations touching core platform systems.
Document and publish reusable patterns for organizational benefit.
Use AI coding tools as a productivity multiplier while maintaining quality and technical judgment.