Artificial Intelligence Engineer

Position Overview\nThe Engineer, Artificial Intelligence supports the development and deployment of advanced artificial intelligence solutions. This role focuses on implementing solutions that leverage Machine Learning, Natural Language Processing, and emerging technologies such as Generative AI and Large Language Models. The Engineer, Artificial Intelligence will work closely with data scientists, ML engineers, and product teams to translate business needs into scalable AI systems.\nEssential Duties\nInclude, but are not limited to, the following:\nSupport the execution of ML, NLP, LLM deliverables aligned with AI strategies.\nContribute to the development of custom ML, Gen AI, NLP, LLM models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), and NLP/LLM model development.\nCollaborate closely with data scientists, machine learning engineers, and software engineers to integrate AI models into production systems.\nStay current with emerging AI/ML technologies and recommend innovative approaches.\nApply best practices in data privacy, security, and biases in AI systems.\nMinimum Qualifications:\nBachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field; or High School Diploma/General Education Degree and 4+ years of relevant as outlined in the essential duties in lieu of Bachelor’s Degree.\nProfessional experience in AI/ML model development.\nDemonstrated ability working with machine learning frameworks, programming languages like Python, and cloud platforms.\nDemonstrated ability to learn new technologies.\nDemonstrated understanding of ethical considerations in AI systems.\nStrong analytical and problem-solving skills with understanding of AI/ML techniques.\nPreferred Qualifications:\nExperience deploying Gen AI solutions at scale.\nExperience fine tuning LLMs, SLMs, teacher-student frameworks and model distillation.\nFamiliarity with human in the loop methods for aligning LLMs with human preferences.\nFamiliarity with agentic framework platforms and concepts.