Senior Applied AI Engineer – Supply Chain
Chicago, IL Hybrid
$75,000–$125,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
As a Senior Applied AI Engineer on our Supply Chain team, you will drive the technical vision and architectural design for integrating foundational models and autonomous systems into our global logistics network. You will own the end-to-end lifecycle of enterprise-grade AI products—evolving conversational interfaces into highly reliable, multi-agent systems that execute complex supply chain decisions in real time. Beyond writing production code, you will architect secure, scalable LLM deployments, design rigorous evaluation frameworks, and mentor junior engineers. You will act as a strategic partner to supply chain leadership, translating massive operational bottlenecks into deployed, autonomous AI solutions. As a Senior Applied AI Engineer on our Supply Chain team, you will drive the technical vision and architectural design for integrating foundational models and autonomous systems into our global logistics network. You will own the end-to-end lifecycle of enterprise-grade AI products—evolving conversational interfaces into highly reliable, multi-agent systems that execute complex supply chain decisions in real time. You will act as a strategic partner to supply chain leadership, translating massive operational bottlenecks into deployed, autonomous AI solutions.
What you'll bring
- 5+ years of software engineering, data engineering, or ML engineering experience, with a proven track record of architecting and deploying LLMs or AI agents into production environments.
- Programming & Systems: Expert-level Python and SQL.
- Deep understanding of distributed systems, microservices, and modern cloud infrastructure (AWS/GCP/Azure).
- Generative AI Expertise: Mastery of LLM orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI), vector databases (e.g., Pinecone, Weaviate), and retrieval optimization techniques (hybrid search, semantic routing).
- Production Engineering: Extensive experience with containerization (Docker, Kubernetes), CI/CD pipelines, API design, and system observability tools.
- Communication: Proven ability to influence product roadmaps, manage technical debt, and communicate complex AI constraints and capabilities to non-technical business leaders.
- Data & Integration: Experience with BI automation, Text-to-SQL workflows, and programmatically generating visualizations or dashboards via API (e.g., Power BI, Tableau, Looker, custom frameworks).
- Familiarity with ETL/ELT pipelines and SQL.
- Infrastructure: Knowledge of containerization (Docker, Kubernetes) and cloud-native architecture on AWS.
- Bachelor's Degree in Computer Science, Artificial Intelligence or related
- Under 10%
- Advanced AI Tooling: Experience building custom evaluation frameworks (e.g., Ragas) or fine-tuning open-source foundation models for domain-specific tasks.