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Llm In Cybersecurity Jobs in California (NOW HIRING)

Fieldguide

Senior AI Engineer

San Francisco, CA · On-site

$190K - $255K/yr

... cybersecurity, privacy, and financial audits. We build software for the people who enable trust ... This role is for engineers who have shipped LLM-powered features in production and are ready to ...

Kai

Senior Software Engineer, AI Platform

San Jose, CA · On-site

$140 - $210/hr

Their confidence in Kai reflects what we've built: an AI‑powered cybersecurity platform that ... You'll design the test infrastructure -- unit tests with mocked LLM responses, integration tests ...

Fieldguide

Senior Analytics Engineer

San Francisco, CA · On-site

$175K - $200K/yr

... in cybersecurity, privacy, and financial audits. Put simply, we build software for the people who ... Experience feeding LLM or AI-native data interfaces (semantic layer, MCP, text-to-SQL guardrails)

Zoomcar

Principal AI Security Engineer

Sunnyvale, CA · On-site

$160 - $220/hr

Strong understanding on LLM Based Application architectures, technologies, frameworks, and ... A BS degree in Computer Science, Cyber Security, other tech-related degree, or equivalent ...

Expedia

Director CyberSecurity

San Jose, CA · On-site

Demonstrated experience applying AI and machine learning techniques within cybersecurity contexts ... Demonstrated proficiency in AI security - implementing controls for LLM applications, RAG systems ...

Expedia Group

Director CyberSecurity

San Jose, CA · On-site

Demonstrated experience applying AI and machine learning techniques within cybersecurity contexts ... Demonstrated proficiency in AI security - implementing controls for LLM applications, RAG systems ...

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Llm In Cybersecurity information

What are the key skills and qualifications needed to thrive as an LLM in Cybersecurity?

To excel as an LLM in Cybersecurity, you need a solid legal education (LLB or JD), expertise in cybersecurity law, and an understanding of digital privacy regulations. Familiarity with legal research databases, cybersecurity frameworks (such as NIST or ISO), and certifications like CIPP/US or CISSP can be highly beneficial. Strong analytical thinking, attention to detail, and effective communication skills set candidates apart in this specialized field. These competencies are critical for interpreting complex legal and technical issues, advising organizations on compliance, and mitigating risks in the rapidly evolving cybersecurity landscape.

How does an LLM in Cybersecurity contribute to collaboration between legal and technical teams within organizations?

An LLM in Cybersecurity equips professionals with both legal expertise and a strong understanding of cybersecurity principles, enabling them to act as a bridge between legal and technical teams. Individuals in this role often translate complex legal requirements into actionable security policies or help IT teams understand compliance obligations. This collaboration is vital for ensuring that security strategies align with regulatory standards and that organizations are protected from both legal and cyber risks. As regulations and technologies evolve, professionals with an LLM in Cybersecurity are well-positioned to facilitate communication and foster a culture of compliance and security.

What is an LLM in Cybersecurity?

An LLM in Cybersecurity is a Master of Laws degree focused on the legal, regulatory, and policy aspects of cybersecurity and information privacy. This postgraduate program is designed for lawyers and legal professionals who want to specialize in areas such as cybercrime, data protection, and technology law. Students typically study topics like digital evidence, cyber risk management, and international cybersecurity law. The degree prepares graduates for roles in law firms, government agencies, and corporate legal departments dealing with cybersecurity issues.

What is the difference between Llm In Cybersecurity vs Cybersecurity Analyst?

AspectLlm In CybersecurityCybersecurity Analyst
Required CredentialsLegal degree, cybersecurity certifications (e.g., CISSP, CEH)Security certifications (e.g., CompTIA Security+, CISSP), relevant experience
Work EnvironmentLegal and cybersecurity teams, policy development, complianceSecurity operations centers, incident response, threat analysis
Employer & Industry UsageLaw firms, corporate legal departments, cybersecurity firmsBusinesses, government agencies, IT firms

While Llm In Cybersecurity combines legal expertise with cybersecurity knowledge, Cybersecurity Analysts focus on protecting systems and responding to threats. Both roles require cybersecurity certifications, but Llm In Cybersecurity emphasizes legal and compliance aspects, whereas Cybersecurity Analysts concentrate on technical security measures.

What are popular job titles related to Llm In Cybersecurity jobs in California? For Llm In Cybersecurity jobs in California, the most frequently searched job titles are:
What job categories do people searching Llm In Cybersecurity jobs in California look for? The top searched job categories for Llm In Cybersecurity jobs in California are:
What cities in California are hiring for Llm In Cybersecurity jobs? Cities in California with the most Llm In Cybersecurity job openings:
Llm In Cybersecurity jobs near you
Infographic showing various Llm In Cybersecurity job openings in California as of August 2026, with employment types broken down into 85% Full Time, and 15% Contract. Highlights an 60% In-person, 14% Hybrid, and 26% Remote job distribution.

Senior Data Engineer, AI Platform

Kai Cyber, Inc.

San Jose, CA • On-site

$124K - $168K/yr

Full-time

Re-posted 5 days ago

Job description

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

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