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Remote Retrieval Augmented Generation Jobs in Michigan

Raas Infotek LLC

Cloud & AI Solutions Architect

Lansing, MI · Remote

$64.50 - $88.25/hr

Architect Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge management. * Design AI-powered copilots, chatbots, virtual assistants, and intelligent automation solutions.

Deloitte

AI and Data Science Engineer III

Detroit, MI · On-site +1

$113K - $136K/yr

Implement retrieval-augmented generation patterns, including document ingestion, chunking, embeddings, vector or hybrid search, and retrieval and evaluation telemetry * Deliver governed datasets and ...

Harris

(Remote) AI Bid & Proposal Manager

Wyoming, MI · On-site +1

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Remote work and more! About Cayenta: Cayenta is a leading provider of enterprise resource ...

Harris

(Remote) AI Bid & Proposal Manager

Wyoming, MI · On-site +1

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Remote work and more! About Cayenta: Cayenta is a leading provider of enterprise resource ...

General Motors

AI Agent Engineer

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 ...

company826

AI/ML and Data Engineer

Southfield, MI · On-site +1

$104K - $125K/yr

Architect and deliver LLM-enabled Generative AI solutions (e.g., Retrieval-Augmented Generation, tool use, and agentic workflows) that enable natural-language access to SME knowledge assets such as ...

General Motors

Senior Software Engineer (.NET )

Warren, MI · On-site +1

$114K - $151K/yr

The ideal candidate will also have hands-on experience with AI agentic workflows, LLM-based automation, retrieval-augmented generation, and intelligent workflow orchestration . You will collaborate ...

All JobsRemote Retrieval Augmented Generation JobsRemote Retrieval Augmented Generation Jobs in Michigan

Remote Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Remote Retrieval Augmented Generation Engineer, and why are they important?

To thrive as a Remote Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and experience with retrieval systems such as Elasticsearch or FAISS are typically required. Problem-solving, effective communication, and adaptability are important soft skills for collaborating remotely and iterating on rapidly evolving AI solutions. These skills ensure the engineer can design, deploy, and optimize robust RAG systems that effectively combine retrieval and generation for high-quality AI outputs.

What is the difference between Remote Retrieval Augmented Generation vs Remote Data Scientist?

AspectRemote Retrieval Augmented GenerationRemote Data Scientist
CredentialsAI/ML knowledge, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development, NLP projectsData analysis, model building
Industry UsageAI, NLP, machine learningTech, finance, healthcare
Search & ComparisonOften compared for AI roles involving language modelsCompared for data analysis roles

Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.

What are some common challenges faced by professionals working in Remote Retrieval Augmented Generation roles, and how can they be addressed?

Professionals in Remote Retrieval Augmented Generation (RAG) roles often encounter challenges related to integrating diverse data sources, ensuring low latency in information retrieval, and maintaining the quality and relevance of augmented outputs. Coordinating effectively with distributed teams and adapting to rapidly evolving AI technologies are also common hurdles. To address these, staying current with best practices in data engineering, leveraging robust APIs, and participating in regular team check-ins can help ensure smooth collaboration and system performance.

What is Remote Retrieval Augmented Generation?

Remote Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models with external information sources. In a remote RAG setup, the model retrieves relevant data from remote databases or APIs during the generation process, enhancing its responses with up-to-date or domain-specific knowledge. This approach is widely used in applications that require accurate, context-aware answers, such as chatbots, search engines, and virtual assistants. By leveraging remote retrieval, RAG systems can access a broader range of information without needing to store all data locally.
What are the most commonly searched types of Retrieval Augmented Generation jobs in Michigan? The most popular types of Retrieval Augmented Generation jobs in Michigan are:
What are popular job titles related to Remote Retrieval Augmented Generation jobs in Michigan? For Remote Retrieval Augmented Generation jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Remote Retrieval Augmented Generation jobs in Michigan look for? The top searched job categories for Remote Retrieval Augmented Generation jobs in Michigan are:
What cities in Michigan are hiring for Remote Retrieval Augmented Generation jobs? Cities in Michigan with the most Remote Retrieval Augmented Generation job openings:
Remote Retrieval Augmented Generation jobs near you
Cloud & AI Solutions Architect

Cloud & AI Solutions Architect

Raas Infotek LLC

Lansing, MI • Remote

$64.50 - $88.25/hr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Title: Cloud & AI Solutions Architect
Experience: 12+ Years
Employment Type: W2

Position Overview

We are seeking a highly experienced Cloud & AI Solutions Architect with 12+ years of experience in designing, architecting, and implementing enterprise-scale cloud and AI solutions. The ideal candidate will possess deep expertise in cloud platforms, artificial intelligence, machine learning, Generative AI, enterprise architecture, and cloud-native technologies.

This role will be responsible for defining cloud and AI strategy, designing scalable architectures, leading digital transformation initiatives, and enabling organizations to leverage AI and cloud technologies to drive innovation and business value.

Key ResponsibilitiesCloud Architecture & Strategy
  • Design and implement scalable, secure, and highly available cloud architectures across AWS, Azure, and/or Google Cloud Platform.
  • Define enterprise cloud adoption strategies, migration roadmaps, and modernization initiatives.
  • Lead cloud transformation programs involving application modernization, containerization, and microservices.
  • Establish cloud governance, security, compliance, and cost optimization frameworks.
  • Architect multi-cloud and hybrid-cloud solutions.
Artificial Intelligence & Generative AI Solutions
  • Design enterprise AI and Generative AI platforms leveraging Large Language Models (LLMs).
  • Architect Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge management.
  • Design AI-powered copilots, chatbots, virtual assistants, and intelligent automation solutions.
  • Evaluate and integrate foundation models such as GPT, Claude, Gemini, Llama, and Mistral.
  • Develop AI governance, responsible AI, and model monitoring frameworks.
  • Lead AI solution deployment and operationalization using LLMOps and MLOps best practices.
Solution Architecture
  • Create end-to-end solution architectures integrating cloud services, AI platforms, APIs, data lakes, and enterprise systems.
  • Design microservices-based and event-driven architectures.
  • Develop architecture standards, reference architectures, and technology roadmaps.
  • Collaborate with business and technical stakeholders to align solutions with strategic objectives.
Data & Analytics
  • Architect modern data platforms supporting AI and analytics workloads.
  • Design data lakes, data warehouses, real-time streaming solutions, and vector search architectures.
  • Implement enterprise data governance, security, lineage, and quality frameworks.
Leadership & Stakeholder Management
  • Serve as a trusted advisor to business leaders, product teams, and technology executives.
  • Lead architecture reviews, technical governance, and solution design workshops.
  • Mentor architects, engineers, and development teams.
  • Drive innovation by evaluating emerging cloud and AI technologies.
Required QualificationsExperience
  • 12+ years of experience in software engineering, cloud architecture, or enterprise solution architecture.
  • 5+ years of experience designing cloud-native solutions.
  • 3+ years of experience implementing AI/ML and Generative AI solutions.
  • Proven experience delivering large-scale enterprise transformation initiatives.
Technical SkillsCloud Platforms

Strong experience with one or more:

  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud Platform (Google Cloud Platform)
Cloud Services

Experience with:

  • Compute Services (EC2, Azure VM, GCE)
  • Kubernetes (EKS, AKS, GKE)
  • Serverless Computing
  • API Management
  • Event Streaming
  • Cloud Security Services
  • Identity & Access Management (IAM)
  • Networking & Infrastructure Design
Artificial Intelligence & Generative AI

Hands-on experience with:

  • Large Language Models (LLMs)
  • Generative AI Applications
  • Retrieval-Augmented Generation (RAG)
  • Prompt Engineering
  • AI Agents & Multi-Agent Systems
  • Fine-Tuning and Model Optimization
  • Semantic Search
  • Knowledge Graphs
AI Frameworks

Experience with:

  • LangChain
  • LangGraph
  • LlamaIndex
  • Semantic Kernel
  • CrewAI
  • AutoGen
Machine Learning

Experience with:

  • PyTorch
  • TensorFlow
  • Scikit-learn
  • Hugging Face Transformers
  • Model Deployment and Monitoring
Programming Languages

Strong proficiency in:

  • Python
  • Java
  • JavaScript/TypeScript
  • SQL
DevOps, MLOps & LLMOps

Experience with:

  • CI/CD Pipelines
  • GitHub Actions
  • Jenkins
  • Terraform
  • Infrastructure as Code (IaC)
  • MLflow
  • Kubeflow
  • Docker
  • Kubernetes
Databases & Data Platforms

Experience with:

  • PostgreSQL
  • MySQL
  • MongoDB
  • Redis
  • Snowflake
  • Databricks
  • Delta Lake
  • Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB, FAISS)
Architecture Expertise

Strong knowledge of:

  • Enterprise Architecture Frameworks (TOGAF preferred)
  • Microservices Architecture
  • Event-Driven Architecture
  • Domain-Driven Design (DDD)
  • API-First Design
  • Cloud-Native Architecture
  • Security Architecture
  • Zero Trust Security Model
  • High Availability and Disaster Recovery
Preferred Qualifications
  • Experience in Financial Services, Banking, Insurance, Healthcare, Retail, or Technology sectors.
  • Experience with Responsible AI and AI Governance frameworks.
  • Knowledge of cloud security standards and compliance requirements.
  • Experience with AI observability and model evaluation platforms.
  • Experience integrating AI solutions with enterprise applications such as Salesforce, ServiceNow, SAP, and Workday.
  • Experience with Data Mesh and modern data architecture patterns.
Certifications (Preferred)Cloud Certifications
  • AWS Certified Solutions Architect – Professional
  • AWS Certified Machine Learning Specialty
  • Microsoft Azure Solutions Architect Expert
  • Microsoft Azure AI Engineer Associate
  • Google Professional Cloud Architect
  • Google Professional Machine Learning Engineer
Architecture Certifications
  • TOGAF Certified
  • Certified Kubernetes Administrator (CKA)
Key Skills Summary

Cloud: AWS, Azure, Google Cloud Platform, Kubernetes, Serverless, Terraform, Cloud Security

AI & GenAI: LLMs, GPT, Claude, Gemini, Llama, RAG, AI Agents, Prompt Engineering, LangChain, LangGraph, LlamaIndex

Data: Databricks, Snowflake, Delta Lake, Data Lakes, Vector Databases

DevOps/MLOps: Docker, Kubernetes, GitHub Actions, Jenkins, MLflow, Kubeflow

Programming: Python, Java, JavaScript, SQL

Architecture: Enterprise Architecture, Microservices, Event-Driven Systems, Cloud-Native Design, Security Architecture

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