Data Management professional at Kyndryl involved in creating innovative data solutions and ensuring the seamless operation of complex data systems. Collaborating with teams to transform requirements into scalable database solutions.
About the role
- Sr Staff AI Data Engineer responsible for implementing AI data pipelines at an insurance company. Collaborating with teams to enhance data capabilities and ensure scalability and reliability.
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.
- 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.
Requirements
- 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.
- 3+ years of AI/ML experience, with 1+ years of data engineering experience focused on supporting Generative AI technologies.
Benefits
- Other rewards may include short-term or annual bonuses
- long-term incentives
- on-the-spot recognition
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