Sr Data Engineer - Hybrid

Overview

Sr Staff AI Data Engineer is responsible for implementing AI data pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.

This role will have a hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).

• AI data engineering lead responsible for implementing AI data pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions, including pre-processing with extraction, chunking, embedding and grounding strategies to prepare data.
• Develop AI-driven systems to improve data capabilities, ensuring compliance with industry’s best practices.
• Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
• Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
• Stay up to date with industry advancements in AI and apply modern technologies and methodologies to our systems.
• Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
• Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics, etc.
• Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management.
• Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.
• Collaborate closely with Dev Ops and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
• Mentor junior team members and promote reusable patterns and standards in delivering high-quality data and AI solutions.
• Develop graph database solutions for complex data relationships supporting AI systems.
• Apply AI solutions to insurance-specific data use cases and challenges.
• Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding integrity and scalability.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.
• 8+ years of hands-on data engineering experience including data solutions, SQL and No
  
  SQL, Snowflake, ETL/ELT tools, CI/CD, Big Data, Cloud Technologies (AWS/Google/Azure), Python/Spark, Datamesh, Datalake or Data Fabric.
• Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or Tensor Flow.
• Experience implementing data governance practices, including data quality, lineage, data catalog capture, strategically and dynamically on a large-scale data platform.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
• Strong written and verbal communication skills and ability to explain technical concepts to various stakeholders.

• Experience in multi-cloud hybrid AI solutions.
• AI Certifications.
• Experience in Employee Benefits industry.
• Knowledge of NLP and computer vision technologies.
• Contributions to open-source AI projects or research publications in Generative AI.
• Experience building AI pipelines that integrate structured, semi-structured and unstructured data, including pre-processing with extraction, chunking, embedding and grounding strategies, semantic modeling, and data readiness for models and agentic solutions.
• Experience with vector databases, graph databases, No
  
  SQL, Document DBs (e.g., AWS Open Search, GCP Vertex AI, Neo4j, Spanner Graph, Neptune, Mongo, Dynamo
  
  DB).
• 3+ years of AI/ML experience, with 1+ years focused on supporting Generative AI technologies.
• Hands-on experience implementing production-ready enterprise-grade AI data solutions.
• Experience with prompt engineering techniques for large language models.
• Experience in implementing Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval mechanisms with language models.
• Experience with vector databases and graph databases, including implementation and optimization.
• Experience in processing and leveraging unstructured data for AI applications.
• Proficiency in implementing scalable AI-driven data systems supporting agentic solutions (AWS Lambda, S3, EC2, Langchain, Langgraph).

The listed annualized base pay range is based on external market analysis; actual base pay may vary based on performance, proficiency and demonstrated competencies. The base pay is part of The Hartford’s total compensation package, with potential bonuses, incentives, and recognition. The annualized base pay range for this role is: $135,040 - $202,560

Equal Opportunity…