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Llm Jobs in Riverside, CA (NOW HIRING)

ISite Technologies Inc

Senior AI Engineer

Irvine, CA

$112K - $154K/yr

The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines. The ideal candidate will own end-to-end delivery across the AI ...

Lendistry

Sr AI/Agentic Engineer

Tustin, CA · On-site

$115K - $158K/yr

Required : • 5+ years of software engineering experience, with 3+ years building and shipping LLM-powered applications in production. • Expert-level Python for production systems -- clean ...

First American

Senior Applied ML Engineer

Santa Ana, CA · On-site

$129K - $189K/yr

Develop end-to-end ML and LLM pipelines, covering data ingestion, scripting, automated workflows for OCR, model training, evaluation, and post-processing in production environments. * Build and ...

First American

Senior Applied ML Engineer

Santa Ana, CA · On-site

$129K - $189K/yr

Develop end-to-end ML and LLM pipelines, covering data ingestion, scripting, automated workflows for OCR, model training, evaluation, and post-processing in production environments. * Build and ...

RAVE Aerospace LLC

AI Developer

Brea, CA · On-site

Build and maintain MCP (Model Context Protocol) servers that expose enterprise systems to LLM clients * Develop end-to-end features -- backend services, data pipelines, and user-facing UIs -- using ...

RAVE Aerospace LLC

AI Developer

Brea, CA · On-site

Build and maintain MCP (Model Context Protocol) servers that expose enterprise systems to LLM clients * Develop end-to-end features - backend services, data pipelines, and user-facing UIs - using ...

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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 Jun 15, 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 are the key skills and qualifications needed to thrive as an LLM (Master of Laws) graduate, and why are they important?

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 is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or chief AI officers, often requiring advanced skills in deep learning, data science, and programming. These positions usually involve leadership, strategic planning, and extensive experience, and may include stock options or bonuses that contribute to the total compensation package.

What can you do with an LLM degree?

An LLM degree qualifies individuals for advanced legal roles such as legal analyst, compliance officer, or law professor. It can also enhance expertise in specialized areas like international law or intellectual property, often requiring strong research, writing, and analytical skills. The degree may lead to opportunities in law firms, corporate legal departments, or government agencies.

What are LLMs (Large Language Models)?

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 jobs can you do with LLM?

With an LLM (Master of Laws), you can pursue careers in legal practice, such as lawyer, legal advisor, or corporate counsel. It also qualifies you for roles in academia, legal research, compliance, and policy analysis, often requiring strong analytical and research skills.

What job makes $10,000 a month without a degree?

A machine learning engineer or AI specialist can earn $10,000 or more per month through expertise in developing and deploying AI models, often requiring strong programming skills in Python and knowledge of frameworks like TensorFlow or PyTorch. Success in such roles depends on experience, project complexity, and the ability to work independently or in high-demand environments, often without formal degrees but with relevant skills and certifications.

What Is an LL.M.?

A Master of Laws, or LL.M., is an advanced legal degree designed for lawyers or legal scholars who want to demonstrate their expertise in a specific area of law after law school. While a juris doctoris (JD) is the most common degree people receive at law school, an LL.M. is a secondary degree that typically takes an additional year to complete. The LL.M. curriculum includes coursework in U.S., Canadian, and international law, and is meant for an attorney wanting to specialize in a specific area of law. An LL.M. also provides foreign attorneys with the necessary skills and background to practice law in the United States.

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 are some common challenges faced by professionals working with large language models (LLMs) 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.
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Llm jobs near you
Principal AI Engineer - Agentic Systems and LLM Platforms

Principal AI Engineer - Agentic Systems and LLM Platforms

TMS

Irvine, CA • On-site

Contractor

Posted 17 days ago

Job description

Role: Principal AI Engineer – Agentic Systems & LLM Platforms 
Duration: 6+ Months C2H
Location: Irvine, CA (Hybrid)
 
 
Job Summary:
 
Required Qualifications:
 
Must Have:
Strong software engineering fundamentals and proficiency in Python plus Java Go TypeScript is a strong plus
Experience of working with Codex.
Proven experience building LLM powered applications in production tool calling function, calling structured outputs retrieval and evaluation.
Experience designing distributed systems and APIs REST, RPC plus event driven patterns Kafka, SQS, Pub Sub.
Solid understanding of data engineering basics SQL data modeling feature engineering and data quality
Handson knowledge of cloud platforms AWS or Azure or GCP containers, Docker and orchestration Kubernetes preferred.
Ability to write clean testable secure code comfortable with code reviews and engineering rigor
Experience with multiagent systems planning verification and autonomous workflow execution
Experience with vector databases hybrid search and knowledge graphs
Familiarity with model evaluation offline evals golden datasets adversarial testing regression harnesses and AB testing.
 
Technical Skills:
Agent frameworks Lang Graph Semantic Kernel similar orchestration frameworks or equivalent custom implementations
RAG tooling embedding pipelines hybrid retrieval reranking chunking strategies citation provenance
Observability Open Telemetry structured logging dashboards alerting
Data systems OLTP analytics warehouses lakes streaming pipelines feature stores optional
Testing unit integration tests for tools replay tests for agent traces eval harnesses for LLM outputs
 
Key Responsibilities:
1. Agentic AI System Design Engineering
Design and implement agent architectures planner executor tool using agents multiagent orchestration reflection evaluation loops.
Build tooling integrations for agents merchant systems underwriting platforms transaction stores risk engines CRM case tools knowledge bases and workflow engines.
Implement robust state management session memory task plans provenance traceability and replay ability of agent actions
 
2. LLM RAG Engineering for Payments Workloads
Develop RAG pipelines over policies SOPs card network rules underwriting guidelines dispute playbooks and merchant agreements.
Apply prompt and system design structured output patterns and schema validation for deterministic agent behaviour.
Optimize for latency cost and reliability using caching model routing and evaluation driven prompt iteration.
 
3. ML Decisioning Integration
Combine LLM agents with classical ML models fraud scoring anomaly detection risk scoring and rules engines.
Build feedback loops from outcomes chargeback win rate false positives approval uplift to continuously improve models and agent strategies.
 
4. Safety Compliance and Responsible AI
Implement guardrails PII handling policy enforcement prompt injection defences tool per missioning rate limiting and safe failover.
Ensure auditability why an agent took action evidence used and human approval where required humanintheloop.
 
5. Productionization MLOps LLMOps
Build CICD for agent services evaluation suites telemetry drift detection and incident response playbooks.
Instrument agent behavior using tracing spans structured logs and metrics task success tool errors hallucination indicators
 
6. Collaboration Leadership
Partner with Product Risk Ops Underwriting Compliance and Engineering to convert business problems into deployable AI solutions.
Mentor engineers set standards for agent design patterns testing and production readiness.