Key skills
AWSAzureCloudGoogle Cloud PlatformJavaPythonSparkSQLAILLMRAGAgenticData EngineeringDatabricksGCPGoogle Cloud
About this role
Role Overview
- Design and develop scalable cloud-native solutions
- Build scalable ingestion pipelines for structured and unstructured data
- Design semantic layers and context-building strategies for LLM consumption
- Architect and build production-ready RAG systems
- Develop embedding pipelines and manage vector databases at scale
- Collaborate with teams to complete assignments on time, with quality
- Be a coach/mentor to junior developers
- Identify risks and drive them to resolution as required
Requirements
- 6+ years of development experience in an enterprise-level engineering environment
- 4+ years of hands-on backend Data Engineering application development experience
- Proven hands-on experience designing, building, and operating data pipelines for LLM-based agentic AI systems
- Expert-level SQL and strong Python proficiency (Java is a plus)
- Experience with distributed processing frameworks (Spark, Databricks, Flink, etc.)
- Experience building data pipelines in cloud-native environments (AWS/GCP/Azure)
- Experience building scalable, fault-tolerant, observable systems
- Good knowledge of Data Structures and Algorithms
- Strong understanding of data modeling and semantic layer design
- Understanding of embeddings, chunking strategies, retrieval optimization, and re-ranking
Tech Stack
- AWS
- Azure
- Cloud
- Google Cloud Platform
- Java
- Python
- Spark
- SQL
Benefits
- Full benefits package
- Exciting travel perks
- Generous time-off
- Parental leave
- Flexible work model
- Career development resources