Senior AI Engineer

National Vision•Duluth, GA

About The Position

We are seeking an innovative Senior AI Engineer responsible for designing, developing, and deploying AI/ML solutions that solve business problems, predict outcomes, identify inefficiencies, and enable data‑driven decision‑making. This role leverages advanced modeling, machine learning pipelines, generative AI, and automation to deliver scalable, enterprise‑ready AI capabilities.

Requirements

4-6 years proven experience in data engineering or data science field (Required)
4-6 years as an AI/ML engineer, in a similar role, or equivalent practical experience, with a strong portfolio of successful projects (including ML, NLP, generative AI, and operationalization in production) (Required)
Four-year college degree or equivalent experience. in Computer Science, Mathematics, Data Science, or relevant discipline (Required)
Proficiency in Python, SQL, and AI/ML frameworks and development platforms (e.g., PyTorch, TensorFlow, Azure OpenAI, Azure Machine learning studio) (Required)
Strong understanding of data structures, data pipelines, and MLOps practices (Required)
Working knowledge of generative AI techniques such as retrieval‑augmented generation (RAG), embeddings and vector search, and context orchestration. (Required)
Proficiency with cloud platforms (e.g. Azure, AWS) (Required)
Experience developing and consuming APIs to integrate AI/ML models into applications. (Required)

Nice To Haves

Master's Degree in Data Science (Preferred)

Responsibilities

Collaborate with cross-functional teams, including data architects, engineers, and business stakeholders, to understand requirements for AI/ML use cases.
Develop, train, evaluate, and optimize AI/ML models using structured and unstructured data.
Analyze data and model outputs to identify patterns, trends, and anomalies that inform feature engineering, model improvements, and system optimization.
Leverage AI/ML platforms to build predictive models and automate business processes.
Design and implement generative AI applications powered by Large Language Models (LLMs), using techniques such as retrieval‑augmented generation (RAG), embeddings, prompt orchestration, and evaluation frameworks.
Partner with Data Engineering, Analytics, and IT teams to integrate models into production systems.
Ensure the robustness, scalability, performance, and security of AI/ML models in production environments.
Monitor, evaluate, and continuously improve AI/ML and LLM‑based solutions in production, including model performance, output quality, reliability, and cost efficiency.
Ensure adherence to AI governance, model documentation, and responsible‑AI practices.
Stay up-to-date with the latest advancements in AI/ML research and industry trends.