Senior LLM Engineer

April 3

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Taller

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Taller is an AI-powered company that specializes in delivering innovative solutions to transform businesses. With over 15 years of expertise, Taller focuses on custom engineering, automation, and AI-driven strategies that help clients increase efficiency, reduce costs, and improve overall performance. Their diverse service offerings include digital engineering, custom software development, and AI/ML implementations across various industries.

Agentic AI • AI-Powered Solutions • AI Systems Engineering • Consulting Services • Custom Software Development

201 - 500 employees

Founded 2008

🤖 Artificial Intelligence

💳 Fintech

☁️ SaaS

📋 Description

• We are looking for a talented and innovative Large Language Model (LLM) Engineer with specialized experience in Retrieval Augmented Generation (RAG) to join our dynamic team. • The ideal candidate will be instrumental in the development, optimization, and deployment of RAGbased LLM solutions that meet various business needs. • This role requires a deep understanding of Data Science, Machine Learning (ML), Natural Language Processing (NLP), and the fundamentals of LLMs. • The successful candidate will have hands-on experience with agent based prompting techniques such as LangChain, API-driven development architectures for AI products, usage of vector databases, and information retrieval pipelines to extract actionable insights. • Additionally, strong listening skills and an ability to understand business requirements are essential for designing effective solutions tailored to stakeholder needs. • Designing RAG Solutions: Develop robust RAGbased LLM solutions that enhance data retrieval processes while ensuring high quality output aligned with business objectives. • Prompt Engineering: Craft effective prompts that efficiently guide LLMs towards generating outputs that fulfill specific business requirements; iterate on prompt designs based on performance metrics. • AI Product Development: Design, develop, and refine AI products based on user feedback and stakeholder requirements; ensure alignment with program priorities through agile methodologies. • Support ML Engineering Teams: Collaborate closely with ML engineering teams during the deployment phase of APIs in production environments; assist in troubleshooting issues related to model performance or integration challenges. • Documentation Creation: Document Generative AI use cases comprehensively; create manuals detailing workflows, methodologies employed in solution design, and best practices adopted throughout the process. • Retrieval Augmented Generation Maintenance: Develop and maintain RAG concepts following established data science principles; ensure adherence to industry standards while fostering innovation. • Pipeline Optimization: Ensure efficient operation of data pipelines and processes according to company security policies; promote Responsible AI practices within all project phases. • Collaboration & Communication: Work closely with cross functional teams including product managers, data scientists, software engineers, and stakeholders to align goals effectively; communicate technical concepts clearly across diverse audiences.

🎯 Requirements

• A minimum of 3 years' experience working with Python for machine learning applications along with expertise in AWS Cloud Computing services. • Familiarity with AWS Bedrock's suite of LLM models along with associated prompting strategies tailored for optimal performance. • At least 1 year of direct experience working specifically within NLP frameworks/LLMs focusing on Retrieval Augmented Generation models. • Proficiency in Python programming language utilized extensively for data science projects involving AI development. • Familiarity with agent based frameworks like LangChain which support advanced interactions between users/models. • Experience leveraging AI training frameworks such as TensorFlow or PyTorch alongside Hugging Face Transformers library for model building/training tasks. • Demonstrated expertise in prompt engineering techniques aimed at finetuning large language models tailored toward specific applications or industries. • Knowledgeable about vector databases including practical experience implementing embedding techniques necessary for enhanced information retrieval capabilities. • Experience utilizing orchestration tools designed specifically for AI product development (e.g., ArgoCD). • Ability to create detailed diagrams illustrating data flows/solutions which can effectively communicate complex ideas/processes among team members/internal stakeholders alike. • Prior experience deploying UI/frontend components related directly back into existing large language model systems would be advantageous.