Senior Data AI Engineer

CNA Insurance • Chicago, IL

3d • $72,000 - $141,000 • Hybrid

About The Position

A senior individual contributor role responsible for designing, building, and operationalizing end-to-end AI and machine learning solutions that accelerate CNA's migration to a modern cloud data lakehouse. The engineer works across structured and unstructured data domains—including documents, images, audio, and transactional records—to unlock analytical value through scalable pipelines, RAG architectures, vector databases, and knowledge graphs. The role may also provide guidance to others in building complex technical capabilities.

The position involves developing scalable ingestion and transformation pipelines, implementing modern lakehouse patterns on Google Cloud Platform, and creating AI retrieval layers and production-ready AI solutions. It also includes identifying and implementing process improvements, proposing tools and frameworks to address business problems, and working effectively with peers and team members in a highly matrixed environment. Additional duties may be assigned.

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, Computational Statistics, Data Science, or a related technical field, or equivalent experience.
• Typically 7+ years of experience in data engineering, Artificial Intelligence, or Machine Learning.
• At least 2 years of coding proficiency in one or more programming languages such as Python, Java, or SQL.
• Deep expertise building scalable ingestion and transformation pipelines for structured and unstructured data, with a strong background migrating workloads from legacy systems to modern cloud platforms.
• Skill parsing and normalizing PDFs, emails, images, and call transcripts using OCR, NLP preprocessing such as tokenization, entity extraction, and summarization, and document chunking strategies optimized for LLM consumption.
• Experience designing and implementing vector databases, embedding pipelines, and knowledge graph structures for RAG and semantic search applications.
• Strong SQL and data analytical skills, including experience building data marts and feature datasets for data science and ML applications.
• Strong Python coding fluency; hands-on experience with BigQuery, Claude Code, RAG architectures, LLMs, ADK, and prompt engineering techniques.
• Expertise building ML platforms and data pipelines at scale; familiarity with major ML algorithms, deep learning, NLP, information retrieval, and data mining techniques.
• Experience with GCP services such as Vertex AI, Dataflow, BigQuery, Cloud Run, and Pub/Sub; comfort with distributed computing frameworks such as Apache Spark and Dataproc.
• Experience managing diverse data sources, including preprocessing, cleansing, and verifying data integrity for data science and ML requirements.
• Demonstrated experience with machine learning, deep learning, information retrieval, NLP, or data mining, particularly applied to unstructured or semi-structured data.
• Hands-on experience with vector databases, embedding models, and end-to-end RAG pipeline design.
• Strong communication and interpersonal skills, with the ability to work effectively with peers and team members in a highly matrixed environment.

Nice To Haves

• Master's degree preferred.
• Experience using Agile methods preferred.
• Insurance industry experience, including familiarity with its products and services, preferred.
• Apache Spark experience preferred for big data processing.
• Applicable GCP or Data Engineering certifications preferred.

Responsibilities

• Design and build AI solutions that accelerate migration of data from legacy systems to the cloud while ensuring scalability, reliability, and governance compliance.
• Design and implement scalable ingestion and transformation pipelines for structured and unstructured data, applying OCR, NLP preprocessing, and document chunking strategies optimized for LLM consumption.
• Implement GCP lakehouse patterns, including data governance, cataloging, and lineage tracking, to make data discoverable, auditable, and suitable for AI/ML workloads.
• Design and implement vector databases, embedding pipelines, and knowledge graph structures for RAG and other AI applications.
• Productionize and operationalize AI solutions and advanced analytics in a DevOps/MLOps environment, including automated testing, monitoring, and rollback capabilities.
• Proactively propose ideas and identify tools and frameworks to turn business problems into analytics solutions.
• Research, identify, and implement process improvements addressing complex technology gaps; build strong knowledge of technology enablers.
• May perform additional duties as assigned.

Benefits

• Comprehensive and competitive benefits package intended to support employees' and their family members' physical, financial, emotional, and social wellbeing.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior