Job Description

Salary: $107,000 - 147,000 per year Requirements:

• We require 9+ years of experience in software, data, or AI engineering, including at least 5 years in AI/ML architecture roles.
• We require proven delivery of production AI solutions in retail, e-commerce, supply chain, or other consumer-facing environments; this background is mandatory.
• We need deep, hands-on experience with the open-source AI/ML stack, such as Hugging Face Transformers, LangChain, LlamaIndex, MLflow, Ray, Feast, Evidently, or similar tools.
• We expect strong Python skills along with practical experience using ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
• We look for experience with modern data architectures, including lakehouse, streaming, and batch processing, as well as platforms like Databricks and Snowflake.
• We require demonstrated ability to design AI observability solutions, including model monitoring, drift detection, and production feedback loops.
• We need working knowledge of AI security threats, including prompt injection, adversarial attacks, and secure LLM deployment practices.
• We require hands-on experience with cloud platforms and managed AI/ML services such as AWS SageMaker, Azure ML, Vertex AI, or equivalent.
• We expect a track record of using AI productivity tools such as Copilot, Cursor, Claude, Glean, or similar platforms in day-to-day architecture and engineering work.
• We need excellent communication skills and the ability to explain complex technical designs to both technical and business audiences.
• We prefer experience building or scaling enterprise AI platforms or AI Centers of Excellence.
• We value contributions to open-source AI projects or published architecture patterns.
• We prefer experience with AI red-teaming, adversarial testing, or formal AI risk assessment frameworks.
• We value familiarity with retail systems such as Manhattan WMS, Blue Yonder, Aptos POS, or Salesforce Commerce Cloud.
• Cloud or AI certifications such as AWS ML Specialty, Azure AI Engineer, or Google Cloud Platform Professional ML Engineer are a plus.

• We define and own the enterprise AI architecture for retail use cases, ensuring alignment with business priorities and our broader technology strategy.
• We create reference architectures, design patterns, and standards for AI/ML and Generative AI solutions, with an open-source-first approach.
• We translate retail challenges across merchandising, supply chain, stores, marketing, and e-commerce into scalable AI blueprints.
• We partner with business and product leaders to identify and prioritize the highest-value AI opportunities.
• We champion open-source AI frameworks and tooling as the default path before considering commercial alternatives.
• We lead the evaluation, selection, and integration of open-source AI and ML frameworks into our enterprise architecture.
• We design reusable patterns for open-source LLM deployment, fine-tuning, and serving.
• We establish governance standards for open-source model usage, including licensing checks, security scanning, and model lineage tracking.
• We build internal expertise around open-source foundations to reduce vendor dependency and speed up experimentation.
• We evaluate emerging open-source agentic frameworks for retail automation use cases.
• We architect end-to-end AI solutions, covering data ingestion, feature engineering, model training, inference, and system integration.
• We design AI systems for search, recommendations, personalization, forecasting, inventory optimization, replenishment, allocation, pricing, markdowns, and assistant/copilot use cases.
• We define integration patterns between AI services and retail platforms such as POS, OMS, WMS, CRM, and e-commerce systems.
• We lead architecture reviews to ensure solutions meet performance, scalability, security, cost, and reliability expectations.
• We define and implement an AI observability framework for monitoring model performance, data drift, prediction quality, and system health across production systems.
• We establish real-time and batch monitoring pipelines and create dashboards and alerts for degradation, data skew, latency breaches, and feature store issues.
• We build feedback-loop capabilities to capture ground truth labels and support continuous evaluation in production.
• We define observability standards for GenAI and LLM solutions, including hallucination tracking, prompt and response logging, latency analysis, and cost attribution.
• We partner with MLOps and Platform Engineering to make observability a foundational requirement from day one.
• We serve as the AI security authority for our organization, owning the threat model for production AI and ML systems.
• We define secure-by-design practices for model development, training data handling, inference APIs, and GenAI integrations.
• We architect protections against prompt injection, model inversion, adversarial inputs, data poisoning, and open-source supply chain risks.
• We establish privacy controls for AI pipelines to support regulatory compliance and internal governance requirements.
• We lead AI red-teaming and adversarial testing to uncover and remediate security gaps before release.
• We partner with Information Security, Legal, and Enterprise Risk to maintain an AI risk register and align our AI posture with the wider cybersecurity framework.
• We define guardrails, content filtering, and human-in-the-loop controls for customer-facing and associate-facing GenAI applications.
• We establish MLOps and AIOps practices, including model CI/CD, automated retraining, monitoring, drift detection, and cost controls.
• We define standards for Generative AI and LLM usage, including multi-RAG architectures, MCP, vector search, prompt orchestration, tool-calling, and agentic workflow patterns.
• We ensure AI solutions follow privacy, security, and responsible AI principles.
• We partner with Security, Legal, and Enterprise Architecture to align solutions with governance and risk standards.
• We mandate and model the daily use of AI-native productivity tools across architecture and delivery work.
• We evaluate, recommend, and govern enterprise tools such as Microsoft Copilot, Cursor, Glean, Claude, and equivalent emerging platforms.
• We define guardrails for enterprise AI tool adoption, including data classification rules for what information can be shared.
• We train and upskill engineering and cross-functional teams to use AI productivity tools effectively and improve delivery speed.
• We work closely with AI Engineers, ML Engineers, Data Engineers, and platform teams to ensure our designs are production-ready and executable.
• We provide hands-on implementation guidance, including reference code, pipelines, schemas, and infrastructure patterns.
• We evaluate and recommend AI SaaS offerings, cloud services, and frameworks across platforms such as AWS, Azure, Google Cloud Platform, Databricks, and Snowflake.
• We lead build-versus-buy-versus-open-source decisions and support vendor selection for AI capabilities.

• AI
• AWS
• Architect
• Azure
• CI/CD
• Cloud
• Copilot
• CRM
• Cursor
• Databricks
• Support
• LLM
• Marketing
• PyTorch
• Python
• Salesforce
• Security
• Snowflake
• TensorFlow
• Fiddler
• Zendesk

More:

We are Five Below, a fast-growing retail company built around our purpose of helping people let go and have fun with amazing, low-priced products that make it easy to say yes to the newest and coolest items. Our culture is powered by big ideas, high energy, passion, and a commitment to creating a workplace that feels like a WOWplace. We value innovation, teamwork, and a strong sense of purpose across our more than 27,000 associates. We also offer a comprehensive benefits program that supports health, financial, and personal wellness, and we are an equal opportunity employer committed to fair hiring practices and reasonable accommodations for individuals with disabilities.

last updated 30 week of 2026

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