Lead Data Engineer – Generative AI

Role Overview

• 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 GCP, Vertex AI, S3, AWS Bedrock, 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
• 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.

Requirements

• 8+ years’ Strong hands-on experience programming skills in Python
• 7+ years of data engineering Strong hands-on experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark
• 3+ years of data engineering experience focused on supporting Generative AI technologies
• 2+ years Strong hands-on experience implementing production ready enterprise grade GenAI data solutions
• 3+ years’ experience in implementing Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval mechanisms with language models
• 3+ years’ Experience of vector databases and graph databases, including implementation and optimization
• 3+ years’ Experience in processing and leveraging unstructured data for GenAI applications
• 3+ years’ Proficiency in implementing scalable AI driven data systems supporting agentic solution (AWS Lambda, S3, EC2, Langchain, Langgraph)
• 3+ years’ 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.

Tech Stack

• AWS
• Azure
• Cloud
• EC2
• ETL
• Google Cloud Platform
• NoSQL
• Python
• Spark
• SQL

Benefits

• Other rewards may include short-term or annual bonuses
• Long-term incentives
• On-the-spot recognition