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Freelance Retrieval Augmented Generation Jobs (NOW HIRING)

Futran Tech Solutions Pvt. Ltd.

Data Scientist

Additionally, experience in building Retrieval-Augmented Generation (RAG) pipelines for search and chat applications is highly desired. Key Responsibilities: * Develop and optimize NLP models for ...

Judge Group, Inc.

AI Architect

The Colony, TX

$170K - $180K/yr

Implement RAG (Retrieval Augmented Generation) patterns using requirements, user stories, APIs, configurations, and test repositories, leverage embeddings and vector search where applicable. Apply ...

Newt Global

AI/ML Engineer

Irving, TX

Experience building Retrieval-Augmented Generation (RAG) systems and production-grade AI applications is highly desirable. Key Responsibilities * Design, develop, and deploy machine learning and ...

Purple Drive Technologies

AI Architect

Boston, MA · On-site

GPT, Claude • Prompt Engineering • RAG (Retrieval Augmented Generation) • AWS Cloud Good to Have • Data Structures & Algorithms • OOP and modular design • CI/CD pipelines • Postman ...

Closure Technologies

AI/ML Engineer

Mclean, VA

Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported ...

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How much do freelance retrieval augmented generation jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for freelance retrieval augmented generation in the United States is $22.97, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $18.75 per hour, depending on experience, location, and employer.

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

To thrive as a Freelance Retrieval Augmented Generation (RAG) Specialist, you need expertise in natural language processing, information retrieval, and machine learning, typically supported by a degree in computer science or related fields. Proficiency with frameworks like Hugging Face Transformers, vector databases (e.g., FAISS, Pinecone), and cloud platforms is often required. Strong problem-solving, effective communication, and adaptability set standout professionals apart in this role. These skills ensure the development and fine-tuning of high-performance RAG systems that deliver accurate, contextually relevant results for clients.

What is a Freelance Retrieval Augmented Generation (RAG) specialist?

A Freelance Retrieval Augmented Generation (RAG) specialist is an independent professional who designs, develops, and implements AI systems that combine retrieval-based methods with generative models. RAG specialists help organizations enhance their applications by integrating large language models (LLMs) with external data sources, allowing the AI to access and utilize up-to-date information beyond its training data. Their work involves tasks such as building pipelines for document indexing and retrieval, fine-tuning models, and optimizing the integration for accuracy and efficiency. Freelance RAG specialists typically work on a contract basis, offering flexibility and expertise for businesses that need advanced AI solutions.

How do Freelance Retrieval Augmented Generation specialists typically collaborate with client teams during a project?

Freelance Retrieval Augmented Generation (RAG) specialists often work closely with client data scientists, engineers, and project managers to understand business requirements and integrate RAG systems into existing workflows. Communication is usually handled through regular virtual meetings, shared documentation, and sometimes real-time collaboration tools. Freelancers are expected to deliver modular, well-documented solutions and provide guidance on optimizing retrieval pipelines or fine-tuning models. This collaborative dynamic ensures that RAG implementations are aligned with client goals and technical standards, while also allowing freelancers to contribute innovative solutions based on their expertise.
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Freelance Retrieval Augmented Generation jobs near you
Infographic showing various Freelance Retrieval Augmented Generation job openings in the United States as of May 2026, with employment types broken down into 16% Internship, 55% Full Time, 18% Part Time, 4% Temporary, 5% Contract, and 2% Summer. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $47,772 per year, or $23 per hour.

Senior Software Engineer - Retrieval-Augmented Generation (RAG)

Elsevier

Philadelphia, PA • On-site

$107K - $171K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job title: Senior Software Engineer II - Retrieval-Augmented Generation (RAG) System

About the role, we are seeking an experienced engineer to work with a team to build and support a healthcare centered production-scale RAG system that combines document retrieval with response generation to deliver accurate, context-aware answers. This engineer we be expected to design, implement, and operate end-to-end RAG pipelines- LLM interaction, API creation, and high-performance, secure delivery of knowledge-grounded capabilities. You will collaborate with data engineers, platform teams, and product partners to ship reliable, scalable, and observable systems.

About the team; This collaborative team is entrusted with building the Next Generation Health Solutions through the utilization of cutting-edge technology.

Role and responsibilities

  • Architecting, implementing, testing, and operating end-to-end RAG workflows:

  • Ingesting and normalizing documents from diverse sources

  • Generating and managing embeddings; index and query vector databases
    Retrieve relevant passages, apply reranking or fusion strategies, and feed prompts to LLMs

  • Building scalable, low-latency services and APIs (Python preferred; other languages acceptable) and ensure production-grade reliability (monitoring, tracing, alerting)

  • Integrating with vector databases and embedding pipelines and optimize for latency, throughput, and cost

  • Designing and implementing ML Ops workflows: model/version management, experiments, feature stores, CI/CD for ML-enabled services, rollback plans

  • Developing robust data pipelines and governance around ingestion, provenance, quality checks, and access controls

  • Collaborating with data engineers to improve retrieval quality (embedding strategies, reranking, cross-encoder models, prompt engineering) and implement evaluation metrics (precision/recall, MRR, QA accuracy, user-centric metrics)

  • Implementing monitoring and observability for RAG components (latency, success rate, cache hit rate, retrieval quality, data drift)

  • Ensuring security, privacy, and compliance (authentication, authorization, data masking, PII handling, audit logging)

Required qualifications

  • 5+ years of professional software engineering experience designing and delivering production systems

  • Strong programming skills (Python required; NodeJs a plus)

  • Deep understanding of retrieval-augmented or application-scale NLP systems and practical experience building RAG-like pipelines

  • Hands-on experience with ML workflow tooling and MLOps concepts (model serving, versioning, experiments, feature stores, reproducibility)

  • Proficiency with cloud infrastructure and modern software practices (AWS/GCP/Azure; Docker; Kubernetes; CI/CD)

  • Strong problem-solving skills, excellent communication, and ability to work with cross-functional teams

  • Familiarity with data governance, privacy, and security best practices

Preferred qualifications

  • Experience with agentic workflow tools (LangGraph) and familiarity with prompt engineering for LLMs

  • Exposure to working with and evaluating different LLMs

  • Knowledge of evaluation methodologies for retrieval and QA systems and the ability to set up A/B tests and dashboards

  • Experience with data processing frameworks (SQL, Pandas, Spark) and working with large-scale data pipelines

  • Background in performance optimization for low-latency AI services (MLflow)

  • Experience with monitoring and logging via New Relic, K9s, Portkey, etc

  • Experience with minimizing token usage and cost optimization

  • Comfortable with design and implementation of security controls for data-intensive AI systems

Elsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. It is one of the largest publishers of academic journals and scholarly literature in the world.

Elsevier operates in various domains, including science, technology, medicine, social sciences, and more. They publish a vast number of peer-reviewed journals covering a wide range of disciplines. These journals act as platforms for researchers and academics to share their findings and contribute to the advancement of knowledge in their respective fields.

U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates. If performed in New Jersey, the base pay range is $107,646 - $171,954. This job is eligible for an annual incentive bonus.

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