Job Description

Role: Senior Full Stack AI Engineer
Location: Tampa, FL (3 Days onsite/week)

Job Description:
We are seeking a highly skilled and motivated AI Engineer with a strong focus on Generative AI and Natural Language Processing (NLP) to join our dynamic team. 
The ideal candidate will be instrumental in designing, developing, and deploying AI use cases that involve searching, summarizing, and creating themes from database and extensive and document repositories. 
This role requires a deep understanding of modern AI techniques, particularly those related to Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). This role will be responsible for writing code, pairing with other developers as appropriate, decomposing acceptance criteria to understand team backlog deliverables, complexities, and risk, while working as a strong contributor on an agile team. 
From a technical standpoint, the Software Engineer has full-stack coding and implementation responsibilities and adheres to best practice principles including modern cloud-based software development, agile and scrum, code quality, and tool usage.

Responsibilities:
• Apply depth of knowledge and expertise to all aspects of the software development lifecycle, as well as partner continuously with stakeholders on a regular basis
• Develop and engineer solutions within an Agile software delivery team, working to collaboratively deliver sprint goals, write code, and participate in the broader technical community and team-level Agile and Scrum processes.
• Contribute to the design, documentation, and development of world-class enterprise applications leveraging the latest technologies and software design patterns.
• Leverage technical knowledge of concepts and procedures within own area and basic knowledge of other areas to resolve issues, as necessary.
• Design and Development: Lead the design and implementation of end-to-end AI/ML pipelines for document understanding, summarization, and theme extraction. This includes data preprocessing, feature engineering, model training, evaluation, and deployment.
• Generative AI Application: Develop and optimize LLM-based solutions for text summarization, content generation, and knowledge extraction from structured/unstructured data.
• RAG System Implementation: Build and maintain robust Retrieval-Augmented Generation (RAG) pipelines, leveraging vector databases and advanced indexing strategies to ensure
accurate and contextually relevant information retrieval.
• Model Tuning and Optimization: Apply advanced GenAI tuning techniques such as QLORA, LORA, and PEFT to fine-tune pre-trained LLMs for specific use cases, optimizing for performance, efficiency, and accuracy.
• Vector Search and Embeddings: Implement and optimize vector search capabilities and embedding pipelines to enhance the efficiency and relevance of document searches and information retrieval.
• Prompt Engineering: Develop and refine prompts to maximize the performance and accuracy of language models.
• Collaboration: Work closely with cross-functional teams, including product managers, AI model teams and other engineers, to understand business requirements and translate them into scalable AI/ML solutions.