Sr Staff Data Engineer - Hybrid

Sr Staff Data Engineer - GE07DE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
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).
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’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 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 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 the integrity and scalability of
the environment.
Qualifications

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.
8+ years of strong hands-on 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.
Strong programming skills in Python and familiarity with deep learning
frameworks such as PyTorch or TensorFlow.
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.
Preferred Qualifications

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.
Experience with building AI pipelines that bring together structured, semistructured 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.). 3+ years of AI/ML experience, with 1+ years of data engineering experience 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 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).
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$135,040 - $202,560
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age