Senior Data Engineer, AI Platform

Inc. (kai)
San Jose, United States of America
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

San Jose, United States of America

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Automated Storage and Retrieval Systems
Azure
Google BigQuery
Program Optimization
Encodings
Data Cleansing
Information Engineering
Data Infrastructure
ETL
Data Security
Data Systems
Data Warehousing
Distributed Data Store
Distributed Systems
Information Retrieval
Python
Machine Learning
Recommender Systems
Cloud Services
Search Technologies
SQL Databases
Data Streaming
Management of Software Versions
AI Infrastructure
Parquet
Google Cloud Platform
Real Time Systems
Large Language Models
Snowflake
Spark
Caching
Generative AI
Indexer
Build Management
Data Lake
AI Platforms
Kubernetes
Low Latency
Apache Flink
Kafka
Spark Streaming
Machine Learning Operations
Stream Processing
Data Pipelines
Databricks

Job description

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, * 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)

Requirements

  • 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

Benefits & conditions

  • 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 company

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.

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