Job Description
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Company Overview
A technology company is building an Operations Cloud designed for physical product businesses to manage the flow of goods, dollars, and data in real time across procurement, inventory, orders, fulfillment, and finance.
The platform is designed to replace spreadsheet-heavy processes and rigid enterprise resource planning systems with a modern, AI-native solution. Its Adaptive Resource Platform and unified operational data model enable teams to deploy quickly, automate workflows, and adapt operations without lengthy re-implementation projects.
The organization has secured significant institutional investment to support the continued development of its platform and is focused on helping fast-growing businesses manage critical operations with greater speed, control, and visibility.
Key Responsibilities
- Own the applied AI architecture end-to-end, including model selection, orchestration, RAG over unified operational data, evaluations, guardrails, and production cost and latency management.
- Build an AI-powered operational interface that enables natural-language search, analysis, and automation using live supply-chain, finance, and operations data.
- Develop AI-assisted tooling that uses foundation models to propose, validate, and apply customer schemas and workflow definitions within production environments.
- Design agentic workflows that operate within enterprise security and operational guardrails.
- Ensure AI workflows are permission-aware, auditable, tenant-isolated, and safe to operate against customer systems of record.
- Build evaluation and observability infrastructure to measure system quality, identify regressions, and proactively detect anomalous or potentially risky behavior.
- Partner with product, design, and customer-facing engineering teams to identify and deliver high-impact AI use cases.
- Mentor engineers and help establish strong engineering practices for building, evaluating, and deploying LLM-powered systems.
- Establish scalable patterns for integrating foundation models with transactional systems and business-critical data.
- Make architectural decisions around AI capabilities based on practical business requirements, reliability, security, and operational considerations.
Required Qualifications
- Proven experience shipping LLM-powered features or AI agents to production and operating them at scale.
- Deep hands-on experience with foundation model APIs, RAG pipelines, tool use/function calling, prompt engineering, and context engineering.
- Experience with fine-tuning foundation models when appropriate for specific use cases.
- Experience building evaluation frameworks and guardrails for non-deterministic AI systems operating on business-critical data.
- Strong backend engineering fundamentals and experience designing reliable production systems.
- Experience working with PostgreSQL and cloud services for reliable, multi-tenant web applications.
- Strong understanding of AI system architecture, orchestration, data retrieval, evaluation, and production observability.
- Experience taking AI products or systems from initial concept to production in ambiguous or rapidly evolving environments.
- Strong judgment when determining where AI can provide value and where traditional software approaches are more appropriate.
- Ability to design secure and reliable AI systems capable of interacting with enterprise data and operational workflows.
Professional Qualities
- Team Player: Brings positivity, openness, and curiosity to collaborative engineering environments.
- Growth Mindset: Continuously looks for opportunities to learn and improve individual, team, and organizational capabilities.
- Craftsmanship: Maintains a strong focus on quality when designing and delivering software and AI systems.
- Customer First: Prioritizes the customer experience and works toward building useful, reliable, and engaging products.
- Pragmatic: Makes practical technical decisions based on the specific circumstances rather than relying solely on theoretical approaches.
- Low Ego: Works collaboratively and supports a team-oriented engineering culture.
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