Detroit, MI · On-site
$59.50 - $77.25/hr
Build Retrieval-Augmented Generation solutions using Azure AI Search, vector databases, embeddings, and enterprise data sources. Develop AI agents capable of interacting with APIs, databases ...
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Detroit, MI · On-site
$59.50 - $77.25/hr
Build Retrieval-Augmented Generation solutions using Azure AI Search, vector databases, embeddings, and enterprise data sources. Develop AI agents capable of interacting with APIs, databases ...
Troy, MI · On-site
$63.33 - $73.33/hr
Design Python-led Retrieval-Augmented Generation (RAG) architectures and multi-turn agent orchestration frameworks. * Develop enterprise prompt engineering standards, cookbooks, and Chain-of-Thought ...
Troy, MI · On-site
$63.33 - $73.33/hr
Design Python-led Retrieval-Augmented Generation (RAG) architectures and multi-turn agent orchestration frameworks. * Develop enterprise prompt engineering standards, cookbooks, and Chain-of-Thought ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
Dearborn, MI · On-site
$96K - $131K/yr
Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. * Proven experience in building and ...
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Dearborn, MI · On-site
$96K - $131K/yr
Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. * Proven experience in building and ...
Dearborn, MI · On-site
$105K - $126K/yr
Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services ...
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Dearborn, MI · On-site
$105K - $126K/yr
Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services ...
Auburn Hills, MI · On-site
$108K - $130K/yr
Key Responsibilities - Design, develop, and support RAG (Retrieval-Augmented Generation) pipelines for the application modernization initiative. - Develop Python-based solutions and data/ML workflows ...
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Auburn Hills, MI · On-site
$108K - $130K/yr
Key Responsibilities - Design, develop, and support RAG (Retrieval-Augmented Generation) pipelines for the application modernization initiative. - Develop Python-based solutions and data/ML workflows ...
Warren, MI · On-site
$150 - $200/hr
AI/ML and platform integration + Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. + Integrate agents with ...
Warren, MI · On-site
$150 - $200/hr
AI/ML and platform integration + Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. + Integrate agents with ...
Warren, MI · On-site +1
Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. * Integrate agents with enterprise systems, APIs, and data sources ...
Warren, MI · On-site +1
Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. * Integrate agents with enterprise systems, APIs, and data sources ...
Auburn Hills, MI · On-site
$60.25 - $77.50/hr
We are seeking an experienced AI/ML Architect to design and develop conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. The ideal candidate will have a strong ...
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Auburn Hills, MI · On-site
$60.25 - $77.50/hr
We are seeking an experienced AI/ML Architect to design and develop conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. The ideal candidate will have a strong ...
Warren, MI · On-site
Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. * Integrate agents with enterprise systems, APIs, and data sources ...
Warren, MI · On-site
Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. * Integrate agents with enterprise systems, APIs, and data sources ...
... Retrieval Augmented Generation (RAG) solution. The ideal candidate should be capable of working across the full AI lifecycle, including ingestion, retrieval, modeling, APIs, testing, deployment ...
... Retrieval Augmented Generation (RAG) solution. The ideal candidate should be capable of working across the full AI lifecycle, including ingestion, retrieval, modeling, APIs, testing, deployment ...
Southfield, MI · Remote
$65 - $84.75/hr
The AI Architect will own and define reference architectures for Retrieval-Augmented Generation (RAG), Conversational AI, Document Intelligence, and Agentic AI, ensuring solutions are scalable ...
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Southfield, MI · Remote
$65 - $84.75/hr
The AI Architect will own and define reference architectures for Retrieval-Augmented Generation (RAG), Conversational AI, Document Intelligence, and Agentic AI, ensuring solutions are scalable ...
Warren, MI · On-site
... Retrieval-Augmented Generation) systems to enhance agent knowledge and context Create generative models for code generation, content creation, and strategic planning within agent frameworks Agent ...
Warren, MI · On-site
... Retrieval-Augmented Generation) systems to enhance agent knowledge and context Create generative models for code generation, content creation, and strategic planning within agent frameworks Agent ...
Warren, MI · On-site
... Retrieval-Augmented Generation) systems to enhance agent knowledge and context Create generative models for code generation, content creation, and strategic planning within agent frameworks Agent ...
Warren, MI · On-site
... Retrieval-Augmented Generation) systems to enhance agent knowledge and context Create generative models for code generation, content creation, and strategic planning within agent frameworks Agent ...
Dearborn, MI · On-site
$112K - $148K/yr
Support implementation of emerging AI architectures, including Retrieval-Augmented Generation (RAG), agentic workflows, Model Context Protocol (MCP), and intelligent workflow automation. * Evaluate ...
Dearborn, MI · On-site
$112K - $148K/yr
Support implementation of emerging AI architectures, including Retrieval-Augmented Generation (RAG), agentic workflows, Model Context Protocol (MCP), and intelligent workflow automation. * Evaluate ...
Troy, MI · On-site
Experiment with and implement Retrieval Augmented Generation (RAG), embeddings, vector databases, and other AI-driven architectures * Create seamless full-stack experiences from database to user ...
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Troy, MI · On-site
Experiment with and implement Retrieval Augmented Generation (RAG), embeddings, vector databases, and other AI-driven architectures * Create seamless full-stack experiences from database to user ...
Retrieval-Augmented Generation (RAG) * Machine Learning lifecycle concepts * Strong communication and collaboration skills with technical and non-technical stakeholders. Preferred Qualifications:
Retrieval-Augmented Generation (RAG) * Machine Learning lifecycle concepts * Strong communication and collaboration skills with technical and non-technical stakeholders. Preferred Qualifications:
Dearborn, MI · On-site
$112K - $148K/yr
Support implementation of emerging AI architectures, including Retrieval-Augmented Generation (RAG), agentic workflows, Model Context Protocol (MCP), and intelligent workflow automation. * Evaluate ...
New
Dearborn, MI · On-site
$112K - $148K/yr
Support implementation of emerging AI architectures, including Retrieval-Augmented Generation (RAG), agentic workflows, Model Context Protocol (MCP), and intelligent workflow automation. * Evaluate ...
New
Exposure to LLM application patterns such as retrieval-augmented generation (RAG), prompt engineering, or evaluating AI output quality. * Hands-on usage of AI tools in your development workflow, such ...
Exposure to LLM application patterns such as retrieval-augmented generation (RAG), prompt engineering, or evaluating AI output quality. * Hands-on usage of AI tools in your development workflow, such ...
Exposure to LLM application patterns such as retrieval-augmented generation (RAG), prompt engineering, or evaluating AI output quality. * Hands-on usage of AI tools in your development workflow, such ...
Exposure to LLM application patterns such as retrieval-augmented generation (RAG), prompt engineering, or evaluating AI output quality. * Hands-on usage of AI tools in your development workflow, such ...
A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.
A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.
To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.
Cities near Troy, MI with the most Retrieval Augmented Generation job openings:
$59.50 - $77.25/hr
Contractor
Re-posted 17 days ago
Job Title: Azure AI Foundry Engineer
Location: Lansing/Detroit, Michigan (Hybrid)
Client: Cognizant
Employment Type: W2
Experience: 8+ Years
Job Description:
We are seeking an experienced Azure AI Foundry Engineer to design, develop, and deploy enterprise-grade generative AI solutions on the Microsoft Azure platform. The ideal candidate should have strong experience with Azure AI Foundry, Azure OpenAI, Retrieval-Augmented Generation, AI agents, Python, and cloud-native application development.
Key Responsibilities
Design and develop generative AI applications using Azure AI Foundry and Azure OpenAI.
Build Retrieval-Augmented Generation solutions using Azure AI Search, vector databases, embeddings, and enterprise data sources.
Develop AI agents capable of interacting with APIs, databases, documents, and other business systems.
Design prompt workflows, system instructions, tool-calling processes, and multi-step agent workflows.
Develop backend AI services and APIs using Python, FastAPI, Flask, or similar frameworks.
Integrate structured and unstructured data from Azure Data Lake, Blob Storage, SQL databases, SharePoint, and enterprise applications.
Implement document ingestion, chunking, embedding, indexing, retrieval, and response-generation pipelines.
Evaluate AI applications for response accuracy, relevance, groundedness, safety, latency, and overall performance.
Implement responsible AI controls, content filtering, prompt-injection protection, access controls, and data-security standards.
Monitor AI applications using Azure Monitor, Application Insights, logging, tracing, and Foundry observability capabilities.
Deploy and manage AI solutions using Azure Functions, Azure App Service, Azure Container Apps, AKS, or similar Azure services.
Build automated CI/CD pipelines using Azure DevOps or GitHub Actions.
Collaborate with data engineers, data scientists, architects, business analysts, and product teams.
Troubleshoot issues related to model responses, data retrieval, integrations, application performance, and cloud deployment.
Prepare technical design documents, architecture diagrams, deployment guides, and operational documentation.
Required Skills
6+ years of overall software engineering, data engineering, cloud, or AI development experience.
Strong hands-on experience with Azure AI Foundry or Microsoft Foundry.
Experience with Azure OpenAI models, embeddings, chat completions, and model deployments.
Strong experience developing RAG-based applications.
Hands-on experience with Azure AI Search, vector search, semantic search, and hybrid search.
Strong programming experience with Python.
Experience developing REST APIs using FastAPI, Flask, or similar frameworks.
Experience with prompt engineering, function calling, tool calling, and AI agent development.
Understanding of LLM evaluation, hallucination reduction, groundedness, responsible AI, and content safety.
Experience with Azure services such as Blob Storage, Data Lake, Key Vault, Functions, App Service, Container Apps, AKS, and Application Insights.
Experience working with SQL, NoSQL, document databases, or vector databases.
Experience with Git, Azure DevOps, GitHub Actions, Docker, and CI/CD processes.
