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Internship Large Language Model Llm Jobs in Missouri

Exavalu

Principal Agentic AI Engineer

California, MO · On-site

$140 - $200/hr

Large Language Model (LLM) applications * Retrieval-Augmented Generation (RAG) * Agentic AI solutions * Strong software engineering fundamentals. * Experience building scalable enterprise AI ...

Jobtailor

Senior Data Scientist II

California, MO · On-site

$120 - $160/hr

Strong foundation in statistics, modeling, and large‑scale text processing Core Competencies Demonstrates expertise in Natural Language Processing (NLP), Large Language Models (LLM), and generative ...

MasterCard

Senior AI Engineer

O Fallon, MO · Hybrid

$97K - $134K/yr

Expert-level, hands-on experience designing, building, and deploying large language model (LLM ... Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty ...

Mastercard, Inc.

Senior AI Engineer

O Fallon, MO · On-site

$97K - $134K/yr

... large language model (LLM) applications, agentic systems, and RAG pipelines - from prototype to ... Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty ...

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Internship Large Language Model Llm information

What is an internship in large language model (LLM)?

An Internship in Large Language Model (LLM) typically involves working with advanced artificial intelligence models like GPT or similar technologies. Interns in this field assist with tasks such as data preparation, model training, evaluation, and deployment of natural language processing applications. They may also contribute to research, experimentation, and development of new model features or performance improvements. This role provides hands-on experience in AI, machine learning, and natural language processing, often requiring knowledge of programming, data science, and AI concepts.

What types of projects do interns typically work on during a large language model (LLM) internship?

During a Large Language Model (LLM) internship, interns often participate in projects such as data preprocessing, fine-tuning models on specific tasks, evaluating model outputs, and developing tools for model interpretability. Interns may collaborate closely with research scientists and engineers, contributing to both experimental and production-level code. These projects provide practical experience with natural language processing pipelines and exposure to the latest advancements in AI, making it a valuable learning opportunity for those interested in a career in machine learning and artificial intelligence.

What are the key skills and qualifications needed to thrive as an internship large language model (LLM) specialist?

To thrive as an Internship Large Language Model (LLM) specialist, you need a solid grasp of machine learning fundamentals, natural language processing, and proficiency in programming languages like Python, often supported by coursework or research in computer science or related fields. Familiarity with tools such as TensorFlow, PyTorch, Hugging Face Transformers, and experience using cloud platforms are typically required. Strong analytical thinking, problem-solving abilities, and effective communication help you collaborate with teams and present complex ideas clearly. These competencies are crucial for developing, evaluating, and refining LLMs to create impactful AI solutions.

What is the difference between Internship Large Language Model Llm vs Data Scientist Intern?

AspectInternship Large Language Model LlmData Scientist Intern
Required CredentialsRelevant coursework, programming skills, knowledge of NLPStatistics, programming, data analysis
Work EnvironmentAI research labs, tech companies, startupsData analysis teams, tech firms, research institutions
Employer & Industry UsageAI development, NLP projects, machine learningData analysis, predictive modeling, business insights

Both roles involve data and programming skills, but Internship Large Language Model Llm focuses on natural language processing and AI model development, while Data Scientist Interns work on analyzing data to generate insights. The choice depends on your interest in AI/NLP versus data analysis and business applications.

What are popular job titles related to Internship Large Language Model Llm jobs in Missouri?

For Internship Large Language Model Llm jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Internship Large Language Model Llm jobs in Missouri look for?

The top searched job categories for Internship Large Language Model Llm jobs in Missouri are:

What cities in Missouri are hiring for Internship Large Language Model Llm jobs?

Cities in Missouri with the most Internship Large Language Model Llm job openings:

Internship Large Language Model Llm jobs near you
Infographic showing various Internship Large Language Model Llm job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 9% Part Time, and 5% Contract. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution.

Principal Agentic AI Engineer

Exavalu

California, MO • On-site

$140 - $200/hr

Other

Posted 19 days ago

Job description

Exavalu Solutions India Pvt Ltd | Full time

Exavalu is a US based consulting firm specialized in Digital Transformation Advisory and Digital Solution Delivery for select industries like Insurance, Healthcare and Financial Services. Our Headquarters is in California, US with multiple offices across US, Canada, and delivery centres in India. We were founded by Industry executives and Consulting principals with deep industry experience that allows us to bring advisory strength and solution expertise to clients. We’re in a hyper growth trajectory growing at over 100% year on year.

Join our diverse and inclusive team where you will feel valued and motivated to contribute with your unique skills and experience.

Exavalu offers permanent remote working model as we believe in going where the right talent is.

Job Description

This is a remote position.

Overview

We are seeking an experienced Agentic AI Technical Lead to lead a high-impact Agentic AI SWAT Pod responsible for delivering next-generation AI solutions across strategic business use cases.

This is a hands-on technical leadership role where you will architect, build, review, troubleshoot, and deploy production-grade AI applications while mentoring a compact engineering team. You will drive the adoption of modern Agentic AI architectures, intelligent workflows, Retrieval-Augmented Generation (RAG), and orchestration frameworks to deliver scalable enterprise AI solutions.

The ideal candidate should possess a strong software engineering background with recent hands-on coding experience in AI applications and have successfully delivered production-grade LLM, RAG, or Agentic AI solutions.

Key Responsibilities
Technical Leadership
  • Lead a compact Agentic AI SWAT Pod across multiple AI use-case tracks.
  • Drive technical architecture and implementation for enterprise Agentic AI solutions.
  • Select the most appropriate solution architecture using:
    • Agentic AI
    • Workflow Automation
    • Retrieval-Augmented Generation (RAG)
    • Application Logic
    • Hybrid AI Patterns
  • Mentor engineers, conduct code reviews, and establish engineering best practices.
  • Parallelize delivery across multiple workstreams while ensuring technical consistency.
AI Solution Development
  • Design and develop production-ready AI agents and autonomous workflows.
  • Build intelligent AI systems using:
    • Large Language Models (LLMs)
    • Agentic AI
    • RAG architectures
    • Multi-agent orchestration
  • Develop reusable AI components and reference implementations.
  • Own critical code paths and contribute to hands-on software development.
Agent Orchestration & Integration

Take ownership of:

  • Agent orchestration
  • Context management
  • AI workflow readiness
  • AI service integrations
  • Design scalable, secure, and maintainable AI platforms.
  • Troubleshoot production issues and optimize AI workloads.
  • Implement CI/CD best practices for AI applications.
  • Build observability into AI systems using modern monitoring frameworks.
  • Ensure engineering quality through testing, code reviews, and deployment automation.
Required Skills & Experience
  • 10+ years of software engineering, platform engineering, or AI development experience.
  • Recent hands-on coding experience in production environments.
  • Large Language Model (LLM) applications
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI solutions
  • Strong software engineering fundamentals.
  • Experience building scalable enterprise AI applications.
  • Strong understanding of API development, cloud-native architecture, and distributed systems.
  • Experience implementing CI/CD pipelines and production monitoring.
Primary Skills
Programming Languages
  • Python
  • Java
  • TypeScript
  • Agentic AI
  • Retrieval-Augmented Generation (RAG)
  • AI Assistants
  • AI Orchestration
  • Prompt Engineering
Frameworks & Technologies
  • LangGraph
  • AI Workflow Orchestration
  • REST APIs
DevOps
  • Git
  • Deployment Automation
Observability
  • OpenTelemetry
  • Performance Optimization
Secondary Skills (Good to Have)
  • LangChain
  • CrewAI
  • AutoGen
  • Semantic Kernel
  • Knowledge Graphs
  • Multi-Agent Systems
  • Docker
  • Distributed Systems
  • MLOps
  • AI Governance
RequirementsRequired Skills & Experience
  • 10+ years of software engineering, platform engineering, or AI development experience.
  • Recent hands-on coding experience in production environments.
  • Large Language Model (LLM) applications
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI solutions
  • Strong software engineering fundamentals.
  • Experience building scalable enterprise AI applications.
  • Strong understanding of API development, cloud-native architecture, and distributed systems.
  • Experience implementing CI/CD pipelines and production monitoring.
Primary Skills
Programming Languages
  • Python
  • Java
  • TypeScript
  • Agentic AI
  • Retrieval-Augmented Generation (RAG)
  • AI Assistants
  • AI Orchestration
  • Prompt Engineering
Frameworks & Technologies
  • LangGraph
  • AI Workflow Orchestration
  • REST APIs
DevOps
  • Git
  • Deployment Automation
Observability
  • OpenTelemetry
  • Performance Optimization
Secondary Skills (Good to Have)
  • LangChain
  • CrewAI
  • AutoGen
  • Semantic Kernel
  • Knowledge Graphs
  • Multi-Agent Systems
  • Docker
  • Distributed Systems
  • MLOps
  • AI Governance

Diversity Inclusion:

At Exavalu, we are committed to building a diverse and inclusive workforce. We welcome applications for employment from all qualified candidates, regardless of race, color, gender, national or ethnic origin, age, disability, religion, sexual orientation, gender identity or any other status protected by applicable law. We nurture a culture that embraces all individuals and promotes diverse perspectives, where you can make an impact and grow your career.

Exavalu also promotes flexibility depending on the needs of employees, customers and the business. It might be part-time work, working outside normal 9-5 business hours or working remotely. We also have a welcome back program to help people get back to the mainstream after a long break due to health or family reasons.

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