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Ai Rag Jobs in Missouri (NOW HIRING)

RAG architectures * Multi-agent orchestration * Develop reusable AI components and reference implementations. * Own critical code paths and contribute to hands-on software development. Agent ...

AI Solutions Lead

Saint Louis, MO · Hybrid

  • Medical

  • Life

  • Retirement

  • PTO

Hands-on delivery of AI, machine learning, generative AI, RAG, automation, or agentic solutions - building, not only advising (Experience as a Forward Deployed Engineer, Customer Engineer, or ...

Test RAG pipelines, prompt engineering, and AI agent orchestration. Execute API testing using Postman, Swagger, and REST APIs. Develop and maintain automation scripts using Python, Selenium ...

Retrieval-Augmented Generation (RAG) * Prompt Engineering Validation * AI Model Validation & Evaluation * API Testing (Postman, REST APIs, Swagger) * Python * Test Automation (Selenium / Playwright ...

Senior AI Engineer

O Fallon, MO · Hybrid

$97K - $134K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Build production-grade RAG and Graph-RAG systems integrating vector databases (Pinecone, pgvector ... Implement Responsible AI and AI governance practices - including bias detection, hallucination ...

Senior AI Engineer

O Fallon, MO · On-site

$97K - $134K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Title and Summary Senior AI Engineer Mastercard's Business & Market Insights (B&MI) group empowers ... RAG and Graph-RAG systems integrating vector databases (Pinecone, pgvector, OpenSearch) and ...

Experience implementing LLM, RAG, Agentic AI, GenAI, or other modern AI solutions in production environments. * Experience with AI orchestration and application frameworks such as LangChain ...

Senior AI Engineer

California, MO · On-site

$187 - $215/hr

  • Medical

  • Dental

  • Vision

Agent architectures, LLM systems, and RAG pipelines * Retrieval, grounding, and orchestration ... Background in AI infrastructure or distributed systems * Exposure to automotive, logistics, or ...

AI Software Engineer

Dearborn, MO · On-site

$110 - $150/hr

Optimize Advanced RAG Pipelines: Implement advanced RAG pipelines (re-ranking, query transformation ... Establish AI Evals to quantify hallucination rates, latency, and cost, leading the shift from ...

Retrieval-Augmented Generation (RAG) architectures * Prompt engineering techniques * Agentic AI concepts and workflows * Build intelligent pipelines using frameworks such as LangChain, LangGraph, and ...

Senior AI/ML Engineer

Saint Louis, MO · On-site

$99K - $136K/yr

... RAG pipelines with hybrid search (semantic + keyword), re-ranking, and Reciprocal Rank Fusion ... AI - Integrate with Azure OpenAI APIs with circuit breaker patterns and fallback chains - Implement ...

Define and own the AI platform architecture: retrieval infrastructure, model lifecycle, evals ... RAG pipelines, vector search, embedding infrastructure, or retrieval‑augmented applications at ...

Principal AI Engineer

California, MO · On-site

$175.80 - $293/hr

This is a hands‑on, full‑stack AI role: you'll work across the entire AI stack from the ... RAG and knowledge services, model/tool routing, prompt and context management, orchestration ...

AI Solutions Architect

California, MO · On-site

$180 - $270/hr

Strong understanding of LLMs, RAG architectures, agentic AI systems, prompt engineering, model fine-tuning, and MLOps. * Databricks: Hands-on experience with Databricks Lakehouse (Delta Lake, Unity ...

RAG, MCP, Agentic systems (design → production) * Security architecture & engineering : Proven track record designing scalable, resilient solutions * Offensive AI security : LLM penetration testing ...

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Showing results 1-20

Ai Rag information

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.

What are popular job titles related to Ai Rag jobs in Missouri?

For Ai Rag jobs in Missouri, the most frequently searched job titles are:

What cities in Missouri are hiring for Ai Rag jobs?

Cities in Missouri with the most Ai Rag job openings:

Principal Agentic AI Engineer

Exavalu

California, MO • On-site

$140 - $200/hr

Other

Posted 6 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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