Senior Data Engineer, AI Platform

We are looking for a Senior Data Engineer (AI Platform) to design and build scalable data systems that power next-generation AI and Generative AI applications.

This is a senior, hands-on technical role for someone who can operate across both classical data engineering and modern AI data infrastructure — including large-scale data pipelines, vector databases, and retrieval systems for LLM-powered applications.

You will work at the intersection of data engineering, AI infrastructure, and LLM systems, enabling high-quality data flow, retrieval, and storage for production-grade intelligence systems.

• Design and build scalable data pipelines for batch and real-time processing
• Develop and maintain data infrastructure supporting AI/ML and Generative AI systems
• Build and optimize retrieval pipelines for RAG and LLM-based applications
• Design and manage vector data pipelines (embedding generation, indexing, storage, retrieval)
• Implement hybrid retrieval systems (BM25 + vector search)
• Work closely with AI/ML teams to enable training, evaluation, and inference workflows
• Develop data models and storage systems optimized for large-scale AI applications
• Ensure data quality, consistency, and reliability across pipelines
• Optimize systems for performance, latency, scalability, and cost
• Collaborate with product, engineering, and AI teams to translate requirements into data solutions

• 4+ years of experience in Data Engineering or related fields
• Strong experience building large-scale distributed data pipelines
• Proficiency in Python and SQL; experience with Spark or similar frameworks
• Experience with both batch and streaming systems (e.g., Kafka, Flink, Spark Streaming)
• Experience working with cloud data platforms (AWS, GCP, Azure)
• Solid understanding of data modeling, storage systems, and distributed systems
• Experience supporting AI/ML workloads through data infrastructure
• Strong ownership mindset and ability to operate in fast-paced environments

• Experience working with LLM-powered systems and RAG pipelines
• Familiarity with vector databases and ANN search systems
• Experience in data systems for AI platforms or ML infrastructure
• Background in search, recommendation systems, or information retrieval

• Batch and streaming pipelines (Spark, Flink, Kafka)
• ETL/ELT design, data modeling, and data warehousing
• Data quality, validation, and observability

• Data pipelines for ML training and inference
• Feature stores and dataset versioning
• Data preparation for LLM and GenAI systems

• ANN algorithms (HNSW, IVF, PQ)
• Hybrid retrieval (BM25 + vector search)
• Embedding pipelines (text, code, image)

• Retrieval pipelines for LLM applications
• Context construction and ranking
• Data indexing and chunking strategies

• Data lakes (S3, GCS, ADLS), Parquet, Delta Lake, Iceberg
• Distributed systems design and scalability
• Caching and low-latency data access

• Kubernetes, Ray (nice to have)

• Query optimization and indexing strategies
• Cost optimization for large-scale data systems
• Latency optimization for real-time retrieval