Senior Data Engineer, AI Platform

Kai is the AI company rebuilding cybersecurity for the machine-speed era. Founded by second time founders and trusted by Fortune 500 enterprises, Kai is building a future where security has no categories, no silos, and no human speed bottlenecks. The Kai Agentic AI Platform replaces fragmented, human-limited workflows with agentic AI systems that continuously contextualize, assess, reason, and execute security work at machine speed - making human defenders, superhuman.

Why Join Kai

• Well-funded: With $125M raised, we have the capital, runway, and resolve to rebuild cybersecurity from first principles.
• Proven: We've earned the trust of Fortune 500 and Global 1000 companies, and we're just getting started. Their confidence in Kai reflects what we've built: an AI-powered cybersecurity platform that performs at the scale and speed the enterprise demands.
• Experienced founders: Our founding team consists of second-time entrepreneurs, each with over 20 years of experience in the cybersecurity industry. Their proven expertise and vision drive our ambitious goals.
• World-class leadership team: Our Heads of AI, Engineering, and Product bring extensive experience from some of the world’s most influential companies, ensuring top-tier mentorship, direction, and vision.
• Frontier AI Applied Research Team: Our researchers operate at the leading edge of agentic AI systems, translating breakthrough capabilities into real-world cybersecurity applications.
• Generous compensation: We offer highly competitive salaries, equity options, and a supportive work environment. Your contributions will be valued and rewarded as we grow together.
  
  

About The Role

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.

Key Responsibilities

• 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
  
  

Required Qualifications

• 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
  
  

Preferred Qualifications

• 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
  
  

Core Technical Expertise

Data Engineering & Pipelines

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

AI Data Infrastructure

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

Vector Databases & Retrieval Systems

• Milvus, Pinecone, Databricks Vector Search, FAISS
• ANN algorithms (HNSW, IVF, PQ)
• Hybrid retrieval (BM25 vector search)
• Embedding pipelines (text, code, image)
  
  

RAG & LLM Data Systems

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

Storage & Distributed Systems

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

Platforms & Infrastructure

• AWS, GCP, Azure
• Databricks, BigQuery, Snowflake
• Kubernetes, Ray (nice to have)
  
  

Performance & Optimization

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