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About This Role
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
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Rippling, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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