Cognitive Knowledge Engineer (AI, LLM, Remote)

About iBusiness Funding iBusiness Funding is a software and lender service provider specializing in small business lending. Our technology, team, and process enable us to support loans from $10,000 to $25 million for our lending partners. Our technology solutions have been proven to quickly scale our clients' portfolios without the need for additional overhead. Our flagship product, LenderAI, features end-to-end lending functionality from sales all the way through servicing To date, we've processed over $11 billion in small business loans and handle more than 1,000 business loan applications daily. Our team is driven by our core values of innovation, integrity, enjoyment, and family. Join us and be part of a team that's transforming the finance industry and empowering businesses to thrive! Position Description We are seeking an experienced expert Cognitive Knowledge Engineer to design, implement, and scale state-of-the-art AI systems that combine large language models (LLMs), advanced retrieval techniques, cognitive memory architectures, including knowledge representation, and data fusion. In this role, you will orchestrate robust data pipelines, architect scalable training data solutions, and build the foundational knowledge bases that power next-generation AI agents. You will collaborate with cross-functional teams to ensure our systems efficiently retrieve, contextualize, and generate accurate information for diverse business applications. Major Areas of Responsibility • Architect, implement, and optimize retrieval-augmented generation (RAG) workflows by integrating local LLMs (e.g., Llama) with retrieval mechanisms (vector search, Elasticsearch, FAISS, Weaviate). • Design, build, and maintain scalable data pipelines for ingesting, transforming, indexing, and retrieving structured and unstructured data from diverse sources. • Design, build, and scale addressable services and tools specifications that can be leveraged by LLMs and Agents to orchestrate workflows. • Orchestrate and scale training data operations, including data curation, versioning, and lineage tracking for large-scale LLM training and fine-tuning. • Develop and maintain ontologies, knowledge graphs, and semantic data models to structure and integrate domain-specific knowledge for improved retrieval and reasoning. • Implement and optimize knowledge retrieval strategies (dense/sparse retrieval, ranking algorithms) to maximize system accuracy and relevance. • Aggregate disparate knowledge bases and heterogeneous data into a fused approach for access to relevant contextual information. • Design cognitive memory systems for AI agents, enabling persistent knowledge retention and contextual awareness across interactions. • Collaborate with AI researchers, data scientists, and engineers to align knowledge architecture with business objectives and ensure data quality. • Evaluate and integrate new technologies and research advancements in LLMs, RAG, information retrieval, and knowledge representation. • Maintain clear and comprehensive documentation of models, pipelines, and workflows. Required Knowledge, Skills, and Abilities • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field. • Proven experience designing and scaling data pipelines and training data workflows for LLMs or similar AI systems. • Strong background in information retrieval systems, vector search technologies, and RAG frameworks (e.g., FAISS, Pinecone, Elasticsearch, Milvus). • Proficiency in programming (Python) and machine learning libraries (TensorFlow, PyTorch). • Experience with ontologies, knowledge graphs, and semantic technologies (RDF, OWL, SPARQL). • Familiarity with distributed data processing and orchestration tools (e.g., Spark, Airflow, Kubeflow). • Excellent analytical, problem-solving, and communication skills. • Ability to work collaboratively in a cross-functional, fast-paced environment. Nice To Haves • Experience with LLM fine-tuning, prompt engineering, and RAG optimization. • Familiarity with data-centric AI principles and training data quality assessment. • Experience with cloud platforms and scalable storage solutions. • Background in cognitive memory architectures or AI agent design. Conclusion: This job description is intended to convey information essential to understanding the scope of the job and the general nature and level of work performed by job holders within this job. This job description is not intended to be an exhaustive list of qualifications, skills, efforts, duties, responsibilities, or working conditions associated with the position. The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, genetic information, or any other characteristic protected by law. Apply tot his job