Lead AI Engineer

About The Company

e.l.f. Beauty, Inc. stands with every eye, lip, face and paw. Our deep commitment to clean, cruelty free beauty at an incredible value has fueled the success of our flagship brand e.l.f. Cosmetics since 2004 and driven our portfolio expansion. Today, our multi-brand portfolio includes e.l.f. Cosmetics, e.l.f. SKIN, pioneering clean beauty brand Well People, Keys Soulcare, a groundbreaking lifestyle beauty brand created with Alicia Keys and Naturium, high-performance, biocompatible, clinically-effective and accessible skincare.

In our Fiscal year 25, we had net sales of $1 Billion and our business performance has been nothing short of extraordinary with 26 consecutive quarters of net sales growth. We are the #2 mass cosmetics brand in the US and are the fastest growing mass cosmetics brand among the top 5. Our total compensation philosophy offers every full-time new hire competitive pay and benefits, bonus eligibility (200% of target over the last four fiscal years), equity, and a hybrid 3 day in office, 2 day at home work environment. We believe the combination of our unique culture, total compensation, workplace flexibility and care for the team is unmatched across not just beauty but any industry.

Visit our Career Page to learn more about our team: https://www.elfbeauty.com/work-with-us

Job Summary:

We are seeking a highly skilled and experienced Lead AI Engineer to lead the design, development, and deployment of advanced AI solutions across our enterprise. The ideal candidate will have a deep understanding of AI/ML algorithms, scalable systems, and data engineering best practices.

Responsibilities

• Design and develop production-grade AI and machine learning models for real-world applications (e.g., recommendation engines, NLP, computer vision, forecasting)
• Lead model lifecycle management from experimentation and prototyping to deployment and monitoring
• Collaborate with cross-functional teams (product, data engineering, MLOps, and business) to define AI-driven features and services
• Perform feature engineering, data wrangling, and exploratory data analysis on large-scale structured and unstructured datasets
• Build and maintain scalable AI infrastructure using cloud services (AWS, Azure, GCP) and MLOps best practices
• Mentor junior AI/ML engineers, guiding them in model development, evaluation, and deployment
• Continuously improve model performance by leveraging new research, retraining on new data, and optimizing pipelines
• Stay current with the latest developments in AI, machine learning, and deep learning through research, conferences, and publications
  
  
  

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning or related field.
• 14+ years of IT experience with a minimum of 6+ years of AI/ML
• Experience in AI/ML engineering, particularly with building LLM-based applications and prompt-driven architectures
• Solid understanding of Retrieval-Augmented Generation (RAG) patterns and vector databases (especially Qdrant)
• Hands-on experience in deploying and managing containerized services in AWS ECS and using CloudWatch for logs and diagnostics
• Familiarity with AWS Bedrock and working with foundation models through its managed services
• Experience working with AWS RDS (MySQL or MariaDB) for structured data storage and integration with AI workflows
• Practical experience with LLM fine-tuning techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA or QLoRA
• Strong understanding of recent AI advancements such as multi-agent systems, AI assistants, and orchestration frameworks
• Proficiency in Python and experience working directly with LLM APIs (e.g., OpenAI, Anthropic, or similar)
• Comfortable working in a React frontend environment and integrating backend APIs
• Experience with CI/CD pipelines and infrastructure as code (e.g., Terraform, AWS CDK)
  
  
  

This job description is intended to describe the general nature and level of work being performed in this position. It also reflects the general details considered necessary to describe the principal functions of the job identified, and shall not be considered, as detailed description of all the work required inherent in the job. It is not an exhaustive list of responsibilities, and it is subject to changes and exceptions at the supervisors’ discretion.

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