About the Role
This role is part of the AI Platform team, which owns the AI services that power Affinity's industry-leading relationship intelligence platform. We extract and retrieve information from billions of structured and unstructured data points to deliver actionable insights to customers.
As a Senior AI Engineer, you will collaborate with machine learning engineers, data engineers, software engineers, and product managers to shape the future of private capital's leading CRM platform. You will design and build LLM-powered AI systems that efficiently uncover insights from compelling business interaction data – an exciting and unique opportunity within the industry.
Responsibilities
- Build RAG systems: Architect, prototype, and deploy RAG pipelines, combining vector search, hybrid retrieval, reranking and contextual compression techniques.
- Build LLM powered agent systems: Contribute to design and orchestration of multi-agent LLM systems using community frameworks and custom orchestration layers.
- Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data.
- Collaborate cross-functionally: Partner with cross-functional (product, infra, data engineering, and software engineering) to build robust, high-scale systems that underlie all of our data processing and ML Operations.
Required Qualifications
- 5+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production.
- Hands on experience with LLM applications in production including prompt engineering and utilizing frameworks for online and offline evaluation.
- Experience with LLM assisted search, such as query understanding and augmentation, text2sql, and entity extraction.
- Experience with vector or graph databases.
- Experience with document chunking, embedding models, and context window optimization.
- Familiarity with metadata-based retrieval and re-ranking strategies.
- Hands on experiences with model evaluation metrics (e.g. perplexity, hallucination rate, factual consistency).
- Familiarity with data security, versioning, and MLOps principles.
Nice to Have Qualifications
- Experience with enterprise AI applications with strict compliance, audit, or legal requirements.
- Experience with dataset engineering, including data curation, augmentation, and synthesis, to assist ML model improvements.
- Experience with multi-modal search.
- Experience with graph based recommendation systems, such as graph NN.
- Experience with developing AI applications powered by agent-based systems.
- Experience with packaging, CI/CD and pipeline automation.
Tech Stack
Our ML pipeline manages multiple Python services that support various AI features, including utilizing OCR to extract information from unstructured data, serving embedding models to vectorize chunks, and ranking a list of recommendations based on relevance and user preference.
How we work
- We iterate quickly. As such, you must be comfortable embracing ambiguity, be able to cut through it, and deliver value to our customers.
- We are candid, transparent, and speak our minds while simultaneously caring personally with each person we interact with.
- We make data-driven decisions and make the best decision for the moment based on the information available.