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Retrieval Augmented Generation Jobs (NOW HIRING)

TekShapers

Gen AI Architect

Raritan, NJ · On-site

Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and enterprise data sources. * Collaborate with business stakeholders, product teams, and engineering ...

CYNET SYSTEMS

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$70 - $75/hr

This role involves applying large language models, retrieval-augmented generation, multi-agent orchestration, and foundation model capabilities to automate and enhance privacy operations. Requirement ...

Tekshapers Inc

Generative AI/ML Engineer

New York, NY · On-site

The ideal candidate will have a strong background in investment banking, hands-on experience with Microsoft Azure OpenAI, and expertise in Retrieval-Augmented Generation (RAG). Key Responsibilities:

Pereview Software

AI Engineer

Dallas, TX · On-site

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Pereview Software

AI Engineer

Dallas, TX · On-site

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Redolent, Inc.

Sr, ML Architect

Washington, DC · On-site

Integrate with large language models (LLMs) and generative AI (GenAI) using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques. * Implement MCP client and server ...

CGI

GenAI Context Engineer

Pittsburgh, PA · On-site

Design and implement enterprise Retrieval Augmented Generation (RAG) architectures for GenAI platforms and applications. . Build and optimize semantic retrieval pipelines, vector search ...

CGI Inc.

GenAI Context Engineer

Pittsburgh, PA · On-site

Design and implement enterprise Retrieval Augmented Generation (RAG) architectures for GenAI platforms and applications. . Build and optimize semantic retrieval pipelines, vector search ...

Pereview Software

AI Engineer

Dallas, TX · On-site

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Carter Support Services

AI/ML Engineer

Burbank, CA · On-site

$111K - $153K/yr

Build and deploy RAG (Retrieval-Augmented Generation) pipelines * Integrate LLMs via APIs (Azure OpenAI preferred) into enterprise applications * Develop and orchestrate agentic AI workflows with ...

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Retrieval Augmented Generation information

What are the typical daily responsibilities of a Retrieval Augmented Generation engineer?

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.

What is a Retrieval Augmented Generation job?

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.

What are the key skills and qualifications needed to thrive in the Retrieval Augmented Generation position, and why are they important?

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.

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Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in the United States as of July 2026, with employment types broken down into 90% Full Time, 8% Part Time, and 2% Contract. Highlights an 77% Physical, 3% Hybrid, and 20% Remote job distribution.

AI Scientist / AI Architect

HCM Staffing and Consulting

Irvine, CA • On-site

Other

Posted 9 days ago

Job description

Work Arrangement

  • Onsite: Yes
  • 2 3 days per week in the client's Irvine office.
  • 1 day per week in the client's Downtown Los Angeles office.
  • 1 day remote.

We are seeking a hands-on AI Scientist / AI Architect with 8+ years of experience in AI/ML, Data Science, or Software Engineering to design and deliver scalable, enterprise-grade AI solutions. The ideal candidate will have extensive experience building LLM-enabled applications, agent-based systems, Retrieval-Augmented Generation (RAG) architectures, and cloud-native AI platforms while collaborating with business stakeholders to drive AI innovation.

Mandatory Areas

  • AI/ML Solution Architecture
  • LLM & Generative AI Development
  • Agent-Based Systems
  • Retrieval-Augmented Generation (RAG)
  • Enterprise AI Integration

Must-Have Skills

  • Python & Backend Development
  • LLM / Generative AI Application Development
  • Agent-Based AI Systems
  • RAG, Vector Databases & Embeddings
  • API Development & System Integration
  • AWS Cloud-Native Development
  • CI/CD & DevOps Practices
  • Observability (Logging, Monitoring & Tracing)

Key Responsibilities

  • Design and develop scalable AI/ML pipelines and intelligent enterprise applications.
  • Build agent-based AI workflows, automation systems, and Retrieval-Augmented Generation (RAG) solutions.
  • Architect and implement LLM orchestration layers supporting content generation, drafting, and editing workflows.
  • Integrate AI solutions with enterprise platforms, APIs, backend systems, and data platforms.
  • Lead solution architecture for large engagements and define architecture principles, patterns, and technology roadmaps.
  • Ensure architecture governance, technical quality, scalability, security, and compliance across projects.
  • Conduct architecture reviews, design reviews, technical audits, and Proof of Concepts (POCs).
  • Translate business requirements into AI-driven technical solutions and collaborate with product, marketing, and business stakeholders.
  • Provide architectural leadership, mentor architects and engineering teams, and support offshore delivery.
  • Support pre-sales activities, technical proposals, project estimation, and solution consulting.
  • Identify technical risks, define mitigation strategies, and ensure successful project delivery.
  • Drive innovation through reusable assets, architecture patterns, knowledge sharing, and technology best practices.

Required Qualifications

  • 8+ years of experience in AI/ML, Data Science, or Software Engineering.
  • Strong Python backend development experience.
  • Hands-on experience with LLM-enabled applications and Generative AI.
  • Experience building agent-based AI systems.
  • Strong expertise in RAG architectures, vector databases, embeddings, and indexing.
  • Experience with API development and backend integrations.
  • AWS cloud-native development experience.
  • Experience with CI/CD pipelines and environment management.
  • Strong understanding of observability, including logging, monitoring, and tracing.
  • Experience deploying machine learning models into production.
  • Experience with enterprise AI workflows, automation, and governance.

Architecture & Delivery Expectations

  • Define architecture for large, multi-technology engagements and serve as the design authority.
  • Ensure adherence to architecture principles, design standards, and non-functional requirements.
  • Develop reusable architecture patterns and enterprise AI solution models.
  • Guide technical teams on architecture, design, and technology adoption.
  • Support project planning, estimation, governance, and stakeholder management.
  • Mentor architects and engineers while contributing to technical capability development.

Domain Experience

  • AI-enabled enterprise workflows.
  • Marketing/content generation platforms (Nice to Have).
  • Financial/Investment domain (Preferred).

Certifications

  • AWS, Machine Learning, or AI certifications are good to have but not mandatory.

Apply