Sr Staff Data Engineer - Hybrid

r 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

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