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Llm Jobs (NOW HIRING)

Wise Skulls

Senior AI/LLM Engineer

Irving, TX · On-site

$100K - $137K/yr

Title: Sr AI/LLM Engineer Location: Irvine, CA (Onsite) Duration: 6 months (possibility of an extension) Implementation Partner: Infosys End Client: To be disclosed JD: Overall 8+ years' experience ...

New

Whatnot

LLM Platform Engineer

San Francisco, CA · On-site

$245K - $345K/yr

Create robust and scalable LLM evaluation frameworks to measure model performance, guide iteration, and prevent regression via CI/CD. * Deploy RAG systems and MCP servers to more effectively ground ...

Horizon3.ai

Staff Attack Engineer, AI/LLM

You'll also help drive our LLM-powered offensive capabilities and act as a technical leader for AI/LLM offense. Essential Functions Attacking AI/LLM Systems * Break AI and agentic systems and ...

Spellbrush

AI Anime Researcher - LLM

San Francisco, CA · On-site

What We're Building We've developed an in-house LLM storytelling system that blends AI, story, and gameplay-going far beyond shallow "chat-only" experiences. The result is an AI companion who ...

Gap International

AI/LLM Engineer

Springfield, PA

As an AI/LLM Engineer, you will lead the design and implementation of advanced systems centered on large language models and natural language understanding. Your work will involve techniques such as ...

Gap International

AI/LLM Engineer

Philadelphia, PA · On-site

As an AI/LLM Engineer, you will lead the design and implementation of advanced systems centered on large language models and natural language understanding. Your work will involve techniques such as ...

SmartIPlace

LLM Engineer[Onsite]

Houston, TX · Remote

$170K/yr

LLM Engineer 6-month contract Houston,TX (Onsite) USC/GC Required Skills & Experience -3+ years of large language modeling experience -5+ years of python experience -Strong problem solving skills and ...

Gap International

AI/LLM Engineer

Springfield, PA · On-site

As an AI/LLM Engineer, you will lead the design and implementation of advanced systems centered on large language models and natural language understanding. Your work will involve techniques such as ...

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How much do llm jobs pay per year?

As of Jul 14, 2026, the average yearly pay for llm in the United States is $142,663.00, according to ZipRecruiter salary data. Most workers in this role earn between $126,000.00 and $157,500.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level positions in artificial intelligence, such as AI research directors, senior machine learning engineers, or AI executives, often requiring advanced skills in programming, data analysis, and deep learning. These roles usually involve leadership, strategic planning, and expertise in tools like TensorFlow or PyTorch, with compensation reflecting experience and impact on business or research outcomes.

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 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 provides specialized knowledge in areas like international law, tax law, or human rights, and often requires strong research, writing, and analytical skills. Graduates may work in law firms, government agencies, or corporate legal departments.

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.

Does LLM mean lawyer?

In the context of a job title, LLM typically refers to a Master of Laws degree, not a lawyer. An LLM credential can enhance legal expertise but does not automatically qualify someone as a practicing attorney. To become a lawyer, one must pass the bar exam and meet licensing requirements in their jurisdiction.

What jobs can you do with LLM?

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

What does LLM mean for AI?

In the context of AI jobs, LLM stands for Large Language Model, which refers to advanced AI systems trained on vast amounts of text data to understand and generate human-like language. Professionals working with LLMs often focus on model training, fine-tuning, and deployment using tools like Python and machine learning frameworks such as TensorFlow or PyTorch.

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.

Is ChatGPT an LLM?

ChatGPT is an example of a large language model (LLM) developed by OpenAI. As an LLM, it is trained on vast amounts of text data to generate human-like responses and is used in various AI applications. Working with LLMs as a job may involve skills in machine learning, natural language processing, and programming.

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 is a LLM in a career?

An LLM in a career context typically refers to a Master of Laws degree, a postgraduate qualification for legal professionals seeking specialization or advanced knowledge in areas such as international law, corporate law, or human rights. It can enhance career prospects, qualify individuals for higher-level positions, or prepare them for academia or legal practice. The degree usually requires completing coursework, research, and a thesis, and may involve passing relevant licensing exams depending on the jurisdiction.

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.

What does LLM mean in Masters?

In the context of a master's degree, LLM stands for Master of Laws, a postgraduate academic degree focused on legal studies. It is often pursued by law graduates seeking specialization or advanced knowledge in areas such as international law, corporate law, or human rights. The program typically requires completing coursework and a thesis or research project.
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Llm jobs near you
Infographic showing various Llm job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, 2% Part Time, and 2% Contract. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution, with an average salary of $142,663 per year, or $68.6 per hour.
Senior AI/LLM Engineer

Senior AI/LLM Engineer

Wise Skulls

Irving, TX • On-site

$100K - $137K/yr

Contractor

This job post has expired today. Applications are no longer accepted.

Job description

Title: Sr AI/LLM Engineer
Location: Irvine, CA (Onsite)
Duration: 6 months (possibility of an extension)
Implementation Partner: Infosys
End Client: To be disclosed
JD:
Overall 8+ years' experience with 5+ years in AI development. PFB the technology skills required. Core Language & Architecture Python 3.11+ Advanced type hints (PEP 484), static typing discipline Async programming (asyncio, async/await, async generators) aiohttp / httpx (async HTTP clients) Pydantic v2 (BaseModel, validation, settings management) Structured logging & tracing patterns Redis (pub/sub, TTL, async clients) REST API design & integration patterns Retry/backoff strategies (Tenacity) Concurrency patterns (parallel tool calls, task orchestration) AI / LLM / Agent Systems LangGraph (state machines, conditional edges, checkpointing) LangChain 0.3.x (LLMChain, StructuredTool, retrievers, prompt templates) ReAct-style agent architectures Tool-based agent design (40+ tool environments) Azure OpenAI / OpenAI APIs (GPT-4o, deployment mgmt, rate limits, token budgeting) Prompt engineering (few-shot, structured output, JSON mode) PydanticOutputParser / structured LLM responses Guardrails / PII redaction patterns Memory abstractions for agents Langfuse (trace instrumentation, evaluation, prompt management) LLM fallback chains & error recovery RAG prompt grounding strategies LLM fine-tuning Neural Network training & tuning Traditional ML models (random forest, k-means clustering, linear regression, etc.) MCP development and consumption Retrieval, Search & RAG Engineering Vector databases (Qdrant and/or Milvus) HNSW indexing parameters Filtering strategies Embedding pipelines (OpenAI ada-002 or equivalent) Batch embedding & re-indexing workflows Hybrid retrieval (BM25 + semantic) Score fusion strategies Cross-encoder reranking (BAAI/bge models) FastAPI-based inference services LangChain retriever abstractions RAG evaluation metrics: Faithfulness Relevance NDCG MRR Trace-level RAG evaluation (Langfuse) Data Engineering & ETL Prefect 2.x / 3.x Flows, tasks, futures Deployments (YAML) Scheduling ETL/ELT design Schema evolution Query optimization OAuth authentication Warehouse/schema management PostgreSQL 16/17 psycopg 3.x Connection pooling SQLAlchemy 2.x (ORM + asyncio) Alembic migrations Advanced SQL Multi-table JOINs CTEs Window functions Timezone conversion Pandas 2.x (complex multi-stage transformations) PyArrow / columnar formats Azure Blob Storage (azure-storage-blob) Document ingestion/parsing: Docling Unstructured python-docx python-pptx DevOps & Platform Docker Linux fundamentals Nice-to-Haves Ray (distributed execution) Columnar performance tuning Network operations domain knowledge NOC / alarm correlation familiarity API & Enterprise Integrations OAuth 2.0 (client credentials flow, token lifecycle) MSAL (browser + service principal flows) Microsoft Graph API SharePoint Outlook Planner OneDrive Pagination App permissions ServiceNow REST API Table API Incident/change mgmt Bulk operations Splunk SDK Saved searches Async queries Log analysis Azure AD app registrations IPAM / OTNA integrations (nice-to-have domain exposure)

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