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The Hartford · 1 month ago

Senior Staff AI Data Engineer - Hybrid

Hartford, CT

Full-time

Hybrid

Senior Level, Lead/Staff

$135K/yr - $203K/yr

8+ years exp

The Hartford is an insurance company dedicated to innovation and excellence. They are seeking a Senior Staff AI Data Engineer to lead the development of AI solutions and data engineering strategies, ensuring the reliability and scalability of data systems while mentoring junior team members.

Auto InsuranceCommercial InsuranceEmployee BenefitsFinanceFinancial ServicesInsuranceLife InsuranceProperty Insurance

No H1B

Responsibilities

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. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready

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 GenAI 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 SnowPipe

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 GenAI 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

Qualification

Data engineering experienceGenerative AI technologiesPython programmingCloud platforms (AWS/GCP/Azure)SQLNoSQLSnowflakeETL/ELT toolsData governance practicesMentoring skillsCollaboration skillsCommunication skillsLeadership skillsTime management

Required

Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position

Bachelor's 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, Python/Spark, Datamesh or Data Fabric

1+ years of data engineering experience focused on supporting Generative AI technologies

Strong hands-on experience implementing production ready enterprise grade GenAI 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 GenAI applications

Proficiency in implementing scalable AI driven data systems supporting agentic solution (AWS Lambda, S3, EC2, Lang chain, 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. This includes pre-processing with extraction, chunking, embedding and grounding strategies, semantic modeling, and getting the data ready for Models and Agentic solutions

Experience in vector databases, graph databases, NoSQL, Document DBs, including design, implementation, and optimization. (e.g., AWS open search, GCP Vertex AI, Neo4j, Spanner Graph, Neptune, Mongo, DynamoDB etc.)

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 or GCP or Azure)

Strong written and verbal communication skills and ability to explain technical concepts to various stakeholders