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Llm Jobs in Riverside, CA (NOW HIRING)

Saransh Inc

AI/ML Architect

Irvine, CA · On-site

$68.50 - $88/hr

LLM & Generative AI Development * Agent-Based Systems * Retrieval-Augmented Systems (RAG) * Enterprise AI Integration * AI/ML, Data Science, AI Architecture, Python, LLM, CI/CD Must Have Skills:

CYNET SYSTEMS

Senior AI Engineer

Irvine, CA · On-site

$56 - $61/hr

Build and operationalize LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings pipelines, and prompt orchestration frameworks. * Design and implement vector search systems ...

CompassX Group

Solution Architect - GenAI

Irvine, CA

$150K - $190K/yr

LLM APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock), vector databases, RAG pipelines, evaluation frameworks, and deployment infrastructure. * Bridge to Enterprise Systems: Integrate GenAI ...

CompassX Group

Solution Architect - GenAI

Irvine, CA · On-site

$67.50 - $89/hr

LLM APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock), vector databases, RAG pipelines, evaluation frameworks, and deployment infrastructure. * Bridge to Enterprise Systems: Integrate GenAI ...

My Office Apps

SEO and Web/Wordpress Specialist

Irvine, CA · On-site

$30 - $42/hr

This role requires a strong background in WordPress , B2B SEO , and digital marketing , along with a deep understanding of Google ranking factors and emerging LLM-driven search authority . The ideal ...

My Office Apps

SEO and Web/Wordpress Specialist

Irvine, CA · On-site

$30 - $42/hr

This role requires a strong background in WordPress , B2B SEO , and digital marketing , along with a deep understanding of Google ranking factors and emerging LLM-driven search authority . The ideal ...

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All JobsLlm JobsLlm Jobs in Riverside, CA

Llm information

See Riverside, CA salary details

$87.2K

$146.3K

$186.7K

How much do llm jobs pay per year?

As of Aug 29, 2026, the average yearly pay for llm in Riverside, CA is $146,315.00, according to ZipRecruiter salary data. Most workers in this role earn between $129,200.00 and $161,500.00 per year, depending on experience, location, and employer.

What is an LLM?

LLMs, or Large Language Models, are advanced artificial intelligence systems designed to understand and generate human-like text based on vast amounts of data. These models, such as OpenAI's GPT series, are trained on diverse datasets and can perform a range of tasks, including answering questions, writing content, translating languages, and more. LLMs work by predicting the next word in a sequence, allowing them to create coherent and contextually relevant responses. They are widely used in applications like chatbots, virtual assistants, and automated content generation.

What are the key skills and qualifications needed to thrive as an LL.M. graduate?

To thrive as an LLM graduate, you need advanced knowledge of legal principles, strong research and analytical skills, and a prior law degree such as an LLB or JD. Familiarity with legal databases, research tools like Westlaw or LexisNexis, and sometimes bar admission or certification in specific jurisdictions is advantageous. Exceptional written and verbal communication, attention to detail, and cross-cultural competence are standout soft skills in this field. These abilities are crucial for interpreting complex legal issues, advising clients, and succeeding in global or specialized legal practice.

What are some common challenges faced by professionals working with large language models and how can they be addressed?

Professionals working with large language models often encounter challenges such as managing computational resource demands, ensuring data privacy, and mitigating biases in model outputs. Collaboration with data engineers and IT teams is essential to optimize infrastructure and streamline model deployment. Staying updated on best practices and regulatory guidelines helps address ethical concerns and improve model performance. Continuous monitoring and iteration are key to maintaining accuracy and relevance in real-world applications.

What is the difference between Llm vs Paralegal?

AspectLlmParalegal
Required CredentialsLaw degree (JD or equivalent), possibly an LLM for specializationAssociate's degree or certificate in paralegal studies
Work EnvironmentLaw firms, corporate legal departments, academiaLaw firms, corporate legal departments, government agencies
Industry UsageLegal practice, academia, researchLegal support, case preparation, client communication

The main difference is that an Llm is an advanced law degree for specialization or academic purposes, while a paralegal provides legal support and case assistance without being licensed to practice law. Both roles work closely within legal environments, but the Llm is more focused on legal expertise and research, whereas paralegals handle administrative and preparatory tasks.

What jobs can I do with a large language model?

A large language model (LLM) can be used in roles such as AI researcher, NLP engineer, data scientist, or machine learning engineer, focusing on developing and deploying AI applications. These jobs typically require skills in programming, data analysis, and understanding of AI frameworks like TensorFlow or PyTorch.

Which large language model is most in demand?

The most in-demand large language models for jobs like LLM development and deployment are OpenAI's GPT-4 and GPT-3, as well as Google's PaLM and Meta's LLaMA. Skills in fine-tuning, prompt engineering, and understanding these models are highly sought after in the AI industry.
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Llm jobs near you
Infographic showing various Llm job openings in Riverside, CA as of August 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution, with an average salary of $146,315 per year, or $70.3 per hour.

Senior AI Engineer (GenAI and Data Platform - AWS)

Irvine, CA • On-site

Saransh Inc
IT Services • 51 - 200 employees
Learn more about Saransh Incexternal

$131K - $173K/yr

Contractor

Re-posted 11 days ago

Job description

Role: Senior AI Engineer (GenAI + Data Platform – AWS)
Location: 4 days a week onsite is must (3 days in Irvine, CA & 1 Day in Downtown, LA, CA)
Job Type: Contract

Role Summary:
  • We are seeking a Senior AI Engineer to design, build, and scale a production-grade Generative AI and Data Platform on AWS.
  • The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines.
Note:
Must Have Skills:
  • Generative AI / LLM (RAG, embeddings, prompt engineering)
  • AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)
  • Vector Search & Retrieval Systems (OpenSearch / vector DB)
  • Graph Databases (Amazon Neptune, knowledge graphs)
  • LLM Frameworks (LangChain / LlamaIndex)
  • Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)
  • Databricks & Apache Spark (data pipelines, embedding pipelines)
  • Backend/API Development (Python, scalable APIs, microservices)

Must Have Certifications:
AWS Certification (Preferred):
  • AWS Certified Solutions Architect OR
  • AWS Certified Machine Learning Specialty OR
  • AWS Data Engineer Certification

The ideal candidate will own end-to-end delivery across the AI lifecycle, including:
  • Data ingestion and knowledge curation
  • Embeddings and retrieval systems
  • Backend services and APIs
  • CI/CD pipelines and deployment
Key Responsibilities:
1. GenAI Enablement & Integration
 
Build and operationalize LLM-powered applications using:
  • Retrieval-Augmented Generation (RAG)
  • Embeddings pipelines
  • Prompt orchestration and evaluation frameworks
  • Design and implement vector search systems using Amazon OpenSearch
  • Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability
Integrate supporting infrastructure:
  • Amazon ElastiCache (Redis) for session state and caching
  • DynamoDB for scalable, low-latency data access
 
Implement agentic workflows using frameworks such as:
LangGraph, AutoGen, CrewAI (or equivalent)
Integrate with LLM frameworks like:
LangChain, LlamaIndex (tool calling, retrieval orchestration, context management)
Define standards for:
Tool integration
Context-sharing patterns (MCP-style designs)
Evaluate LLM models and retrieval strategies across:
Latency
Cost
Accuracy
Context limitations
 
2. Data Pipelines & Knowledge Engineering
Design and build scalable data pipelines using Databricks and Apache Spark
Implement:
  • Data ingestion and transformation pipelines
  • Document processing (chunking, metadata tagging)
  • Embedding generation and indexing
Ensure high data quality standards:
Validation, completeness, consistency, monitoring
 
Implement data governance frameworks:
  • Data classification and access controls
  • Retention policies
  • Auditability and lineage tracking
3. Backend Services & APIs
Develop backend services exposing AI capabilities through secure and scalable APIs
Define best practices for:
API contracts and versioning
Reliability (retry logic, circuit breakers, idempotency)
Enable reusability of platform capabilities across teams and applications.
 
4. Deployment, MLOps & Operational Excellence
Build and manage CI/CD pipelines for AI and data workloads
Deploy production systems using:
Docker (containerization)
Kubernetes (orchestration)
Implement deployment strategies:
Blue/green deployments
Canary releases
Rollback strategies
Feature flags
Ensure system reliability through:
Monitoring (latency, failures, cost, data freshness)
Alerting and observability
Secrets management and least-privilege access
Optimize platform performance and cost
 
5. LLM Observability, Evaluation & Quality
Define and track GenAI quality metrics:
Grounding / faithfulness
Retrieval relevance
Response consistency
Latency and cost per request
Implement:
Prompt/version tracking
Offline evaluation pipelines
Continuous improvement workflows
 
6. LLM Security, Safety & Compliance
Implement secure AI systems with:
Access control and authentication
Data protection policies
Responsible AI guardrails
Ensure compliance with best practices in:
AI safety
Data privacy
Monitoring and auditability
Required Skills:
  • Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering)
  • Hands-on experience with AWS ecosystem
Expertise in:
  • OpenSearch (vector search)
  • Neptune (graph databases)
  • DynamoDB and Redis (ElastiCache)
Experience with:
  • LangChain / LlamaIndex
  • Agentic AI frameworks (LangGraph, AutoGen, CrewAI)
  • Strong programming skills (Python preferred)
  • Experience with Databricks and Apache Spark
Solid understanding of:
  • Data pipelines
  • Distributed systems
  • API design
Preferred Skills:
Experience with:
  • Model evaluation frameworks and LLM observability tools
  • AI governance and compliance frameworks
  • Kubernetes and advanced MLOps practices
Familiarity with:
  • Model Context Protocol (MCP) patterns
  • Agent-based architectures
Qualifications:
  • Bachelor’s or Master’s degree in: Computer Science / Data Science / AI / related field
  • Proven experience building production-grade AI platforms and systems
  • Strong background in end-to-end AI/ML lifecycle delivery.