Highlights
Advanced Data Pipelines, Cloud Technologies, Real-Time Streaming, AI Integration, Scalable Solutions
Description
Job Summary
pWe are seeking a dynamic Data and AI Engineer to join our team. This role involves implementing advanced data pipelines, integrating AI technologies, and building scalable real-time data streaming systems. Ideal candidates will have strong experience in cloud platforms, data engineering, and AI-driven solutions.
Responsibilities
- Implementing AI data pipelines for structured, semi-structured, and unstructured data integration.
- Building efficient Retrieval-Augmented Generation (RAG) architectures and integrating them with enterprise data infrastructure.
- Developing scalable real-time data streaming pipelines using Apache Kafka, AWS Kinesis, or similar technologies.
- Creating data domains and products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics, etc.
- Developing AI-driven systems to enhance data capabilities.
- Ensuring the reliability, availability, and scalability of data pipelines through effective monitoring, alerting, and incident management.
- Collaborating with DevOps and infrastructure teams for seamless deployment and maintenance of data systems.
Required Skills
- Data Engineering
- AI Technologies
- Cloud Platforms (AWS/Google/AZURE)
- Prompt Engineering Techniques
- Retrieval-Augmented Generation Pipelines
Required Skills Explained
- Data and AI Engineering: Implementing AI data pipelines to integrate structured, semi-structured, and unstructured data.
- RAG Architectures: Building Retrieval-Augmented Generation architectures to enhance AI systems with effective retrieval mechanisms.
- Real-time Data Streaming Pipelines: Developing scalable real-time data streaming solutions using technologies like Apache Kafka, AWS Kinesis, or Spark streaming.
- Data Domain Development: Creating and maintaining data domains for various consumption archetypes including reporting, data science, analytics, and AI/ML.
- Azure/AWS/GCP Cloud Technologies: Working with cloud platforms to support AI technologies and data systems.
- Prompt Engineering Techniques: Applying advanced techniques to large language models for better performance.
- AI-driven Data Systems: Implementing AI solutions that support agentic solutions using AWS Lambda, S3, EC2, Langchain, or Langgraph.
- Python Programming: Utilizing Python for data engineering and AI development tasks.
- Vector Databases and Graph Databases: Designing, implementing, and optimizing vector databases like AWS OpenSearch, graph databases such as Neo4j, or document databases including MongoDB and DynamoDB.
Who is this for
pThis position is ideal for individuals with a strong background in data engineering and AI technologies. Candidates should have experience working in cloud platforms, implementing AI solutions, and building scalable real-time data pipelines.
Why This Job is a Good Opportunity
ulliGrowth Potential: Join a dynamic team with opportunities for professional development and career advancement.liInnovative Environment: Be part of an organization dedicated to innovation in data engineering and AI technologies.liTalent Development: Work alongside experts who can guide your growth and provide mentorship.liImpactful Work: Contribute to solutions that help shape the future of insurance, making a real difference in people's lives.liVibrant Culture: Enjoy a supportive work environment where teamwork is celebrated and goals are achieved together.
Interview Preparation Tips
- Focus on Key Skills: Highlight your experience with data engineering, AI technologies, and cloud platforms. Prepare examples of projects you've worked on that align with these areas.
- Data Structures & Algorithms: Be ready to discuss advanced concepts in data structures, algorithms, and their applications in real-world scenarios.
- Cloud Platform Knowledge: Demonstrate your understanding of Azure, AWS, or GCP by discussing relevant projects or experiences.
- Prompt Engineering Techniques: Show familiarity with prompt engineering techniques for large language models, including how they enhance AI systems.
- Real-time Data Streaming: Prepare to explain the implementation and benefits of real-time data streaming pipelines using technologies like Apache Kafka or AWS Kinesis.
Career Growth in This Role
pThe role of an IND Associate Engineer, Data offers numerous opportunities for career growth. With a focus on AI data engineering, you can advance into more specialized roles such as Chief Data Officer, Head of AI Engineering, or Data Science Lead. The ability to work with cutting-edge technologies and contribute to innovative projects will make you highly sought after in the industry. Continuous learning and staying updated with the latest trends in data engineering and AI will further enhance your career prospects.
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Skills
Frequently Asked Questions
What kind of experience is required?Candidates need at least 2 years of data engineering experience, including cloud technologies and AI-driven systems.
Do I need a specific degree to apply?A bachelor's degree in computer science or related field is preferred. Advanced degrees may be considered with relevant experience.
What are the key responsibilities of this role?Implementing AI data pipelines, building real-time data streaming systems, and developing scalable solutions for various consumption archetypes.
