Sr Staff AI Data Engineer - Hybrid

The Hartford-posted 3 months ago
$135,040 - $202,560/Yr
Full-time • Senior
Hybrid • Charlotte, NC
5,001-10,000 employees
Insurance Carriers and Related Activities
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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.
  • Develop AI-driven systems to improve data capabilities, ensuring compliance with industry 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 DevOps and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
  • Mentor junior team members and engage in communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
  • Develop graph database solution 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 the integrity and scalability of the environment.
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.
  • 8+ years of data engineering experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.
  • 3+ years of AI/ML experience, with 1+ years of data engineering experience focused on supporting Generative AI technologies.
  • Strong 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 of 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 solution (AWS Lambda, S3, EC2, Langchain, Langgraph).
  • Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with building AI pipelines that bring together structured, semi-structured and unstructured data.
  • Experience in vector databases, graph databases, NoSQL, Document DBs, including design, implementation, and optimization.
  • Experience in implementing data governance practices, including Data Quality, Lineage, Data Catalogue capture, holistically, 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 natural language processing (NLP) and computer vision technologies.
  • Contributions to open-source AI projects or research publications in the field of Generative AI.
  • Short-term or annual bonuses.
  • Long-term incentives.
  • On-the-spot recognition.
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