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Llm Agent Jobs (NOW HIRING)

LLM Agent Systems : Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks * Efficient LLM ...

Understanding of LLM agent frameworks (LangChain, Semantic Kernel, LlamaIndex). * Experience with API integration , JSON schemas, and backend services. * Familiarity with Docker , basic DevOps, and ...

Intern, AI Engineering

San Francisco, CA · On-site

$19.75 - $25.50/hr

LLM Agent Systems : Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks * Efficient LLM ...

Intern, AI Engineering

San Francisco, CA

$19.75 - $25.50/hr

LLM Agent Systems : Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks * Efficient LLM ...

Operationalize LLM / agent solution patterns with enterprise controls: evaluation, PII boundaries, human oversight, cost/latency budgets, red-team expectations. Qualifications Required qualifications ...

Operationalize LLM / agent solution patterns with enterprise controls: evaluation, PII boundaries, human oversight, cost/latency budgets, red-team expectations. Qualifications Required qualifications ...

Solutions Architect

Columbia, MD · On-site +1

$150K - $170K/yr

Operationalize LLM / agent solution patterns with enterprise controls: evaluation, PII boundaries, human oversight, cost/latency budgets, red-team expectations. Qualifications Required qualifications ...

AI/LLM Agent and MCP (Model Control Protocols) - Google ADK, Copilot Studio * Cloud Experience - Google Cloud or Azure preferred. * Database Knowledge - BigQuery, Firestore, Cloud SQL, etc. * Data ...

AI Product Designer

San Francisco, CA · On-site

$135K - $175K/yr

Fluency in LLM/agent concepts (prompt patterns, tool use/function calling, RAG trade-offs, evals) and how they inform UX and product design. * Skilled in product analytics, experimentation, and AI ...

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Llm Agent information

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How much do llm agent jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for llm agent in the United States is $16.10, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $17.31 per hour, depending on experience, location, and employer.

What is the difference between Llm Agent vs Data Scientist?

AspectLlm AgentData Scientist
Required CredentialsTypically a background in AI, machine learning, or related fields; often requires knowledge of NLP and AI frameworksUsually a degree in data science, statistics, or computer science; certifications in data analysis or machine learning are common
Work EnvironmentPrimarily in AI development teams, tech companies, or research labs; focuses on designing and deploying AI agentsIn diverse settings including tech firms, finance, healthcare; analyzes data to inform business decisions
Employer & Industry UsageUsed by AI-focused companies, startups, and research institutionsEmployed across industries like finance, healthcare, marketing, and tech

While both roles involve working with data and advanced technologies, Llm Agents specialize in developing AI agents that utilize large language models, whereas Data Scientists focus on analyzing data to extract insights. The roles often overlap in skills but differ in their primary focus and application.

More about Llm Agent jobs
What cities are hiring for Llm Agent jobs? Cities with the most Llm Agent job openings:
What states have the most Llm Agent jobs? States with the most job openings for Llm Agent jobs include:
Infographic showing various Llm Agent job openings in the United States as of June 2026, with employment types broken down into 98% Full Time, and 2% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $33,480 per year, or $16.1 per hour.

Senior AI Engineer (LLM & Agent Systems) - Platform

Calliere

New York, NY • On-site

Full-time

Posted 5 days ago


Job description

Role Summary This role focuses on designing and scaling reusable AI-driven workflows, particularly those powered by large language models and autonomous agents. You will build foundational components and abstractions that enable multiple internal teams to rapidly develop and deploy intelligent systems. The position emphasizes system reliability, evaluation rigor, and thoughtful tradeoffs in model and tooling selection.

Core Ownership Areas Develop reusable agent-based workflows to accelerate delivery across multiple projects. Define and maintain evaluation standards to ensure consistent model performance over time. Improve system reliability across key dimensions such as accuracy, latency, and robustness.

Build shared APIs and platform components used broadly across engineering teams. Key Responsibilities Design and implement orchestration patterns for LLM-powered agents. Evaluate and select models, tools, and providers based on performance, cost, and reliability.

Build testing frameworks, evaluation pipelines, and monitoring systems for AI outputs. Implement safeguards, fallback mechanisms, and cost optimization strategies. Collaborate with platform and backend engineers to integrate AI capabilities into scalable services.

Identify repeatable patterns across projects and convert them into reusable platform features. Requirements Required Experience Strong background in building production-grade distributed systems or platform infrastructure. Practical experience developing and deploying LLM-based or agent-driven systems.

Demonstrated ability to design for reliability, observability, and cost efficiency. High standards for code quality and system design. Nice-to-Have Experience Familiarity with retrieval systems, embeddings, or context management pipelines.

Experience working within regulated or security-conscious environments. Approach to Work Prioritizes measurable quality through structured evaluation and testing. Designs systems for reuse, scalability, and clean abstraction layers.

Focuses on building solutions that generalize beyond a single use case or team. Benefits - Hybrid onsite. - Incredible perks and comp package.