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Freelance Retrieval Augmented Generation Jobs (NOW HIRING)

AI Engineer

Dallas, TX ยท 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 ...

AI/ML Engineer

Minneapolis, MN ยท Remote

$106K - $131K/yr

Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using vector databases. 6. Automation Development: Creating AI-powered automation solutions, including Einstein ...

The ideal candidate will possess strong expertise in Python development , LLM integration , retrieval-augmented generation (RAG) , chatbot development , workflow automation , and AI model deployment ...

This role focuses on developing AI applications powered by large language models (LLMs), retrieval-augmented generation (RAG), Model Context Protocol (MCP) servers, and Agentic AI across the ...

Sr. AI Developer w/ Reactjs

Warren, NJ ยท On-site

$56.50 - $74.75/hr

The ideal candidate will have strong skills in Python, React.js, Retrieval-Augmented Generation (RAG ), and hands-on experience with LangChain and LangGraph frameworks. You will be responsible for ...

Software Engineer (Java + GenAI)

San Jose, CA ยท On-site

$60.75 - $83.25/hr

... Retrieval-Augmented Generation (RAG) - Vector databases - Prompt engineering - Large Language Models (LLMs) - Application: Send suitable profiles and contact details to rams@vensoft.com

Senior Machine Learning Engineer

OR ยท On-site +1

$205K - $270K/yr

Architect and scale LLM and retrieval-augmented generation pipelines that ground models in enterprise data. This track focuses on building high-performance ML systems that process complex data ...

AI Lead

Chicago, IL ยท On-site

$144K - $177K/yr

The ideal candidate will bring deep expertise in Python, FastAPI, and Retrieval-Augmented Generation (RAG) solutions, with hands-on experience deploying scalable AI applications on Azure. This role ...

This role will focus on practical applications of AI, including Retrieval-Augmented Generation (RAG), the design of sophisticated grounding data structures, and the construction of robust pipelines ...

Gen AI Developer

Nashville, TN ยท On-site

$50/hr

Strong experience designing and implementing Retrieval-Augmented Generation (RAG) architectures. * Experience building vector embedding and vector search pipelines. * Strong cloud-native application ...

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

As of Jun 14, 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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Infographic showing various Freelance Retrieval Augmented Generation job openings in the United States as of June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $47,772 per year, or $23 per hour.
Senior Machine Learning Engineer - Search AI, BLAW/BTAX/BGOV

Senior Machine Learning Engineer - Search AI, BLAW/BTAX/BGOV

Bloomberg LP

New York, NY โ€ข On-site

$134K - $176K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 22 days ago


Job description

Senior Machine Learning Engineer - Search AI, BLAW/BTAX/BGOV
Location
New York
Business Area
Engineering and CTO
Ref #
10051660
Description & Requirements
Bloomberg Law/Tax/Gov is transforming the legal industry through advanced AI-powered research and analytics solutions. Our platform combines sophisticated search, retrieval, and generative AI capabilities to provide fast, reliable access to legal content, company information, analytics, and real-time answers. We are focused on redefining how professionals conduct research and make decisions by building intelligent systems that automate workflows, surface actionable insights, and deliver trusted answers grounded in authoritative content.
As large language models and Retrieval-Augmented Generation (RAG) technologies reshape enterprise search and knowledge discovery, we are investing in next-generation AI systems that combine state-of-the-art machine learning with scalable information retrieval infrastructure. Our goal is to build best-in-class AI experiences that are accurate, explainable, performant, and deeply integrated into the workflows of our clients.
Who are we?
Bloomberg Law/Tax/Gov's Search AI Team is a group of Machine Learning Engineers passionate about solving complex problems in the legal/tax domains using cutting-edge AI technologies. Our team develops large-scale machine learning and retrieval systems leveraging Natural Language Processing (NLP), Natural Language Understanding (NLU), Information Retrieval (IR), and Retrieval-Augmented Generation (RAG) techniques. We work on problems including semantic search, entity resolution, ranking and recommendation systems, query understanding, document understanding, and grounded generative AI experiences.
We work in an agile environment, partnering closely with our product teams, software and ML engineering teams, and legal domain experts to rapidly design, build, and deploy AI-powered solutions at scale.
We'll trust you to:
  • Advance our query understanding framework by improving query parsing, entity linking, and named entity recognition to deliver more accurate and context-aware retrieval experiences
  • Design and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines, including ingestion, indexing, retrieval, re-ranking, and LLM-based answer generation
  • Personalize search and generative AI experiences by analyzing user workflows and adapting retrieval and ranking strategies to individual user needs
  • Improve information and content discoverability by building semantic, ranking, and retrieval models that surface relevant and trustworthy content
  • Design and implement evaluation frameworks that leverage implicit and explicit user feedback to continuously measure and improve retrieval quality, answer relevance, and overall user experience
  • Apply state-of-the-art NLP, NLU, Information Retrieval, and generative AI techniques to deliver grounded, accurate, and explainable answers from large and complex legal content sets
  • Architect and develop scalable backend services and APIs that support high-throughput, low-latency AI and retrieval workloads across distributed systems
  • Operate and optimize scalable systems for handling search queries while meeting stringent SLAs for latency, availability, and reliability
  • Collaborate closely with product managers, software engineers, and domain experts to bring AI-powered capabilities into production
  • Design, write, test, and maintain modular, production-quality code while contributing to engineering best practices across the team
You'll need to have:
  • 4+ years of industry experience developing and deploying machine learning systems in production environments
  • Strong understanding of machine learning fundamentals, information retrieval concepts, and modern NLP techniques
  • Working knowledge of common ML frameworks such as PyTorch, Tensorflow, Scikit-learn and willingness to learn new technologies as needed
  • Familiarity with vector search technologies and search platforms such as Solr/OpenSearch, FAISS, Pinecone, or similar technologies
  • Strong programming skills in Python, Java, or similar languages
  • Experience working with large language models and embedding-based retrieval systems
  • Experience designing scalable backend systems and APIs in distributed environments
  • Familiarity with cloud infrastructure and modern engineering practices, including AWS, Docker, Kubernetes, and CI/CD workflows
  • Curiosity to solve complex technical problems and continuously learn emerging AI technologies
  • Passion for building intelligent systems that deliver measurable impact to end users
We'd love to see:
  • Prior experience working on enterprise search, knowledge discovery, or question-answering systems
  • Experience evaluating and improving RAG systems using ranking metrics, user feedback, or automated techniques
  • Experience optimizing ML systems for latency, throughput, and scalability
  • Familiarity with recommendation systems, ranking models, or personalization frameworks
  • Exposure to legal, financial, or other complex domain-specific content ecosystems

Bloomberg is an equal opportunities employer, and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Salary Range = 165,000 - 260,000 USD Annual + Benefits + Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.

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About Bloomberg

Sourced by ZipRecruiter

Bloomberg runs on data. As the Data Management & Analytics team within Engineering, we support our organization's needs around managing data efficiently. The vision of the team is to build solutions that drive data quality, data dictionary, data stewardship, data lineage, reference, and master data management across various data domains (prospect, customer, vendor, material etc.). We partner with business teams across the organization in addressing their data needs and ultimately helping run business operations efficiently and make improved decisions.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1981