Senior Software Engineer, AI

Stord•Atlanta, GA

About The Position

Stord is seeking a skilled AI-focused Senior Software Engineer who combines deep technical expertise with practical AI/ML integration experience. You'll work directly with our Dir of AI Products to architect and build AI-powered features, collaborate with Data Scientists to understand model requirements, and partner with ML Engineers to create seamless integration between our ML infrastructure and core services. This is a hands-on role where you'll build the backbone services that make AI features accessible across our entire platform. In this role, you'll be instrumental in making AI a first-class citizen in our broader platform. You'll work on critical features like demand forecasting APIs, intelligent routing services, and real-time prediction engines, with the opportunity to shape how AI integrates with our core logistics platform (and what tools we use internally!). This is a unique opportunity to have a massive impact on a team that's transforming supply chain operations through AI.

Requirements

Expert Elixir/Phoenix (2+ years) - You've built and scaled production Elixir systems
TypeScript (2+ years) - You can switch between the best tool for the given task
Distributed Systems - Strong understanding of fault tolerance, supervision trees, and OTP patterns
API Design & Integration - Experience building and consuming RESTful APIs and event-driven architectures
Database Expertise - Advanced SQL skills with PostgreSQL, experience with AlloyDB preferred
Message Queues - Experience with Kafka, RabbitMQ, or similar streaming platforms
Cloud Platforms - Hands-on experience with GCP (preferred), AWS, or Azure
Performance Optimization - Experience optimizing high-throughput, low-latency systems
Production Mindset - You prioritize reliability and user impact over perfect code
Systems Thinking - You understand how AI features fit into the broader platform architecture
Strong Communication - Can explain technical decisions and trade-offs to various audiences
Collaborative Approach - Enjoys working in a collaborative environment
Learning Agility - Comfortable with evolving AI/ML technologies and tools

Nice To Haves

Experience with LLM integration (OpenAI, Anthropic Claude, etc.)
TypeScript experience (we work across Elixir, TypeScript and Python and generally try to choose the best tool for the job)
Cloudflare Workers/AI experience (or other edge platforms)
Event sourcing
Knowledge of feature stores and real-time ML serving
Experience with Modal.com, Vertex AI, or similar ML platforms
Familiarity with logistics, e-commerce, or supply chain domains
Experience with GenAI applications and prompt engineering
Knowledge of vector databases and semantic search
Contributions to open source Elixir, TypeScript or AI projects
Experience with A/B testing and experimentation platforms

Responsibilities

Build robust Elixir services that consume ML predictions from Modal.com, Vertex AI, and other ML platforms
Integrate existing (or new!) products with edge-based inference where appropriate
Create fault-tolerant systems that gracefully handle service failures and provide intelligent fallbacks
Implement caching and optimization strategies for AI feature serving
Build real-time data pipelines using Kafka and Elixir processes to feed ML models
Develop Demand Planning services that integrate forecasting models with inventory management
Build intelligent routing systems that leverage ML predictions for optimal logistics decisions
Create real-time anomaly detection services for supply chain monitoring
Implement AI-powered recommendation engines for logistics optimization
Design and build APIs for LLM integration (OpenAI, Anthropic, Workers AI, etc.) with proper rate limiting and error handling
Design distributed systems that can handle millions of AI-powered logistics operations
Implement proper monitoring, alerting, and observability
Build developer tools and abstractions that make AI capabilities easy for other engineers to integrate
Create configuration management and feature flagging for experiments
Optimize performance and costs across the platform
Partner with the Dir of AI Products on architecture decisions and technical strategy
Collaborate with Data Scientists to understand model inputs, outputs, and constraints
Work with the ML/Data Engineers to ensure seamless model deployment integration
Support product teams in implementing AI features within their domains
Mentor other engineers on AI tool usage and integration patterns and best practices