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Remote Rag Jobs in Chicago, IL (NOW HIRING)

paradigm

Principal AI Architect - Remote

Lombard, IL · Remote

We are seeking a full-time, remote Principal AI Architect. The Principal AI Architect role provides ... Apply hands-on expertise to build LLM-based systems, RAG pipelines, AI agents, and multi-agent ...

paradigm

Principal AI Architect - Remote

Lombard, IL · Remote

We are seeking a full-time, remote Principal AI Architect. The Principal AI Architect role provides ... Apply hands-on expertise to build LLM-based systems, RAG pipelines, AI agents, and multi-agent ...

CoSourcing Partners

GCP Gemini AI Developer

Chicago, IL · On-site +1

Remote / Hybrid - Chicago preferred Employment Type: Contract / Full-Time Reports To: GCP Technical ... Reusable components adopted in at least 3 future use cases. 3. RAG / Context-Aware Workflows ...

Performacentric

Full Stack Developer

Chicago, IL · On-site +1

Remote (Preferred: Philippines, Latin America, or North America) Employment Type: Full-Time / ... Build retrieval-augmented generation (RAG) solutions. * Support prompt engineering and AI workflow ...

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How much do remote rag jobs pay per hour?

As of Jun 21, 2026, the average hourly pay for remote rag in Chicago, IL is $22.15, according to ZipRecruiter salary data. Most workers in this role earn between $18.56 and $23.51 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Rag, and why are they important?

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What is a Remote RAG (Retrieval-Augmented Generation) specialist?

A Remote RAG specialist is a professional who works with Retrieval-Augmented Generation (RAG) systems, typically in the field of artificial intelligence and machine learning. RAG combines traditional information retrieval techniques with generative models like large language models to provide more accurate and contextually relevant answers to user queries. Remote RAG specialists often build, fine-tune, and maintain these systems while working from a remote location. They may also work on integrating RAG models into applications, improving retrieval accuracy, and customizing outputs based on user needs.

What are some common challenges faced by professionals working in a remote RAG (Responsible AI Governance) role?

Professionals in remote RAG roles often encounter challenges related to cross-functional collaboration and maintaining clear communication, especially when working across different time zones. Ensuring alignment on ethical AI standards and compliance requirements can be complex, as it typically involves coordinating with data scientists, legal teams, and business stakeholders. Staying current with evolving regulatory frameworks and best practices in AI governance is also essential, demanding continuous learning and adaptability. Building trust and rapport within a remote team can require extra effort, but leveraging digital collaboration tools and regular check-ins can help mitigate these challenges.
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Principal AI Architect - Remote

paradigm

Lombard, IL • Remote

Other

Posted 3 days ago

Job description

We are seeking a full-time, remote Principal AI Architect. The Principal AI Architect role provides enterprise leadership for the design and delivery of end-to-end AI, Generative AI, and agentic AI solutions, while also contributing hands-on technical expertise to prototyping, implementation, and architectural direction. This position requires deep expertise in enterprise software engineering, cloud architecture, AI/ML, and Generative AI, with responsibility for translating evolving AI capabilities into scalable, secure, and production-ready solutions. The role operates within regulated environments and is accountable for incorporating data privacy, security, governance, and responsible AI practices into solution design and delivery, particularly in healthcare or other sensitive-data domains. This position plays a key role in shaping Paradigm’s AI architecture, advancing the AI Center of Excellence (COE), and enabling consistent, governed, and scalable AI solution delivery across the organization.

RESPONSIBILITIES:  

AI Architecture & Solution Delivery

  • Architect and deliver end-to-end AI, Generative AI, and agentic AI solutions from concept through production
  • Apply hands-on expertise to build LLM-based systems, RAG pipelines, AI agents, and multi-agent orchestration solutions
  • Design AI platform capabilities including model selection, LLM routing, retrieval strategies, memory systems, and tool/function orchestration
  • Lead hands-on prototyping and proof-of-concepts to validate technologies and accelerate adoption
  • Ensure AI solutions are designed for performance, scalability, observability, privacy, and operational readiness
  • Define and drive architecture across multiple domains/business segments, ensuring alignment with enterprise strategy
  • Partner with business and technology leadership to shape AI roadmap, priorities, and execution strategy
  • Establish and promote architecture standards, reusable patterns, and best practices
  • Drive modernization initiatives to reduce technical debt and improve scalability, resilience, and performance
  • Implement and guide AI governance, security, responsible AI, and compliance practices
  • Ensure AI solutions are designed for performance, observability, privacy, and operational readiness
  • Collaborate across engineering, data, product, and business teams to deliver production-grade AI solutions
  • Mentor engineers and architects and effectively communicate AI concepts to technical and non-technical stakeholders

Leadership & Collaboration

  • Partner with business and technology leadership to define AI strategy, roadmap, and execution priorities.
  • Establish and promote architecture standards, reusable patterns, and best practices.
  • Mentor architects and engineers; build internal capability for AI solution delivery.
  • Communicate complex AI concepts effectively to both technical and non-technical stakeholders.
  • Leads adoption of AI-enabled tools within the team, ensuring effective integration into workflows. Coaches employees on appropriate usage, monitors impact on productivity and quality and identifies opportunities for process improvement.
  • Demonstrates a customer-first mindset by developing a broad and deep (where appropriate) understanding of Paradigm organization, products, operations, and customers. Prioritizes collaboration to meet customer needs and expectations and takes personal accountability for service quality.

Technology Strategy & Innovation

  • Continuously evaluate the evolving AI landscape (LLMs, agents, frameworks, tools).
  • Translate emerging technologies into practical enterprise use cases and capabilities.
  • Drive modernization initiatives to improve scalability, resilience, and performance.

QUALIFICATIONS:

  • 10+ years of experience in software engineering, architecture, and enterprise system design.
  • Proven experience delivering end-to-end AI/ML and Generative AI solutions in production environments.
  • Strong hands-on engineering capability with ability to operate across architecture, design, and implementation.
  • Deep expertise in LLMs, prompt engineering, RAG, embeddings, vector databases, and agent-based systems
  • Experience with agent frameworks and orchestration including multi-agent patterns and integrations
  • Strong programming experience in Python and building APIs, microservices, and distributed systems.
  • Experience designing and implementing solutions on cloud platforms (Azure preferred).
  • Experience with DevOps practices, including CI/CD, containerization, and scalable deployment of AI systems.
  • Familiarity with infrastructure-as-code (Terraform, Bicep) and Kubernetes-based deployments.
  • Strong understanding of data architecture and integration patterns supporting AI workloads.
  • Ability to evaluate emerging technologies and translate them into enterprise-scale capabilities
  • Solid knowledge of AI governance, risk, compliance, privacy, and responsible AI principles
  • Strong communication and stakeholder management skills.
  • Ability to influence decisions across engineering, data, and business teams.
  • Proven ability to mentor, guide, and elevate engineering and architecture teams.
  • Master’s or Bachelor’s degree in Computer Science, Engineering, Data Science, or a related STEM field.

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