Senior AI Engineer

In this role you will:

• 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.

Qualifications

Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role, but your past experience doesn’t perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Required:

• 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:

• 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.