Note: The job is a remote job and is open to candidates in USA. Lyric is a leading healthcare technology company committed to simplifying the business of care. The Lead AI Engineer will drive the development of intelligent systems for data extraction from unstructured documents and lead the design of advanced machine learning and generative AI solutions.

Responsibilities

• Lead the architecture, development, and deployment of AI/ML systems for document ingestion, understanding, and data extraction
• Build and create good datasets and a system of good validation and verifications of data and ML systems
• Build and fine-tune LLMs and generative AI models to interpret, summarize, and extract information from complex unstructured content
• Develop NLP pipelines leveraging techniques such as OCR, entity recognition, text classification, summarization, and semantic parsing
• Integrate LLMs with retrieval systems (RAG), vector databases, and structured outputs suitable for downstream consumption
• Collaborate cross-functionally to align technical solutions with product requirements and compliance needs
• Mentor a team of AI/ML engineers, establish best practices in model training, evaluation, and monitoring
• Stay abreast of the latest advancements in generative AI and apply cutting-edge techniques to real-world document challenges

Skills

• Minimum of seven (7) years of experience in AI/ML engineering, with at least three (3) years in a technical or team leadership role
• Previous Technical Leadership in the AI/ML leadership space
• Hands-on experience building and deploying S/LLMs or generative AI applications (e.g., using Llama, Deepseek or similar frameworks)
• Proven track record of extracting structured data from unstructured document sources, including scanned forms, free-text reports, and complex layouts
• Strong software engineering skills in Python and ML frameworks (e.g., Kubeflow, PyTorch, multi-agentic frameworks)
• Experience with OCR technologies (e.g., Tesseract, Amazon Textract), NLP techniques, and model deployment in production environments
• Deep understanding of NLP methods including embeddings, transformers, named entity recognition (NER), and text classification
• Familiarity with MLOps, version control, CI/CD, and cloud platforms (AWS, GCP, or Azure)
• Experience implementing retrieval-augmented generation (RAG), prompt engineering, or fine-tuning foundation models
• Familiarity with vector databases (e.g., Postgres-pg-vector, Pinecone, FAISS, Weaviate) and semantic search
• Strong experience shipping production ML systems with a track record of monitoring and improving the ML systems
• Experience working in regulated domains such as healthcare, legal, or finance

Company Overview

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