AI Engineering Lead

Skims•Los Angeles, CA

12h•Onsite

About The Position

SKIMS is a solutions-oriented brand creating the next generation of underwear, loungewear, shapewear, and essentials. We are setting new standards by delivering technically innovative, comfort-driven products with elevated design for everybody. At the intersection of culture, innovation, and commerce, SKIMS is a global brand redefining modern apparel. Our teams are built on creativity, collaboration, and operational excellence, and we are committed to fostering an inclusive, high-performance environment where ambitious, curious, and values-driven individuals can do the best work of their careers. As we continue to scale globally, our HQ teams play a critical role in shaping strategy, building world-class infrastructure, and driving sustainable growth across markets. We are looking for a talented Lead AI Engineer to help define, design, build, and deploy enterprise-grade AI solutions that drive meaningful business outcomes. In this role, you will provide technical leadership for AI initiatives, mentor engineers, and partner closely with product managers, business stakeholders, and engineering teams to deliver AI-powered products from concept to production, driving adoption, and measurable business impact at scale.

Requirements

8+ years of software engineering experience with recent experience leading enterprise AI initiatives from concept through production.
Proven experience leading the design, development, and deployment of enterprise AI solutions at scale, including large cross-functional initiatives.
Strong understanding of LLM fundamentals, including model training concepts, fine-tuning, evaluation, RAG, and agentic workflows.
Expert-level hands-on experience building AI applications using Python.
Experience with Anthropic Claude models, Claude Code, and AI-assisted software development workflows.
Experience deploying and managing models through AWS Bedrock.
Familiarity with AI observability and evaluation platforms such as Langfuse
Experience building multi-agent systems using frameworks such as LangGraph, LangChain, MCP, or similar technologies
Strong software engineering background with API development, distributed systems, and cloud-native architectures
Experience integrating AI solutions with enterprise systems (ERP, Oracle, Snowflake, internal APIs)
Demonstrated ability to evaluate model performance, optimize costs, and establish AI governance and monitoring practices
Proven track record of identifying AI opportunities, delivering production-ready solutions, and driving adoption across business functions
Security-first mindset with experience designing AI solutions that meet enterprise requirements for security, privacy, governance, compliance, and auditability
Ability to influence technical and non-technical stakeholders and drive alignment across multiple organizations
Passion for software craftsmanship, maintainable system design, testing, observability, and continuous improvement

Responsibilities

Lead the architecture, design, development, and deployment of scalable AI applications and agentic workflows using Python and modern AI frameworks.
Drive technical strategy and best practices for building reliable, secure, and high-performance AI solutions that support critical business operations and growth initiatives
Partner with cross-functional stakeholders to translate business requirements into AI-powered products and lead delivery from ideation through production
Provide technical leadership and mentorship to engineers, fostering engineering excellence and continuous learning across AI initiatives
Develop and maintain integrations with AWS Bedrock, enterprise systems, APIs, and cloud-native architectures
Establish standards for model evaluation, cost optimization, monitoring, governance, and observability across AI systems
Lead the design and implementation of multi-agent systems using frameworks such as LangGraph, LangChain, MCP, or similar technologies
Champion AI-assisted development tools and modern engineering practices to improve productivity, software quality, and delivery velocity
Contribute to architecture reviews, technical roadmaps, documentation, testing, deployment, and continuous improvement initiatives