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Temporary Retrieval Augmented Generation Jobs (NOW HIRING)

Compunnel

Senior Machine Learning Engineer

Raleigh, NC · On-site

$101.60K - $139.50K/yr

Build and operate Retrieval-Augmented Generation (RAG) pipelines, agent orchestration workflows, and retrieval systems. * Develop and maintain Python services supporting AI-driven customer-facing ...

Compunnel

Senior Machine Learning Engineer

Raleigh, NC · On-site

$101.60K - $139.50K/yr

Build and operate Retrieval-Augmented Generation (RAG) pipelines, agent orchestration workflows, and retrieval systems. * Develop and maintain Python services supporting AI-driven customer-facing ...

Datum Technologies LLC

AI / ML Engineer

Charlotte, NC · On-site

Implement LLM-based workflows, including prompt engineering, evaluation, and retrieval-augmented generation (RAG). * Build and maintain knowledge retrieval pipelines to support IVR use cases such as ...

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 ...

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 ...

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

As of May 28, 2026, the average hourly pay for temporary retrieval augmented generation in the United States is $20.86, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $24.28 per hour, depending on experience, location, and employer.

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

AspectTemporary Retrieval Augmented GenerationData Scientist
Required CredentialsTypically requires knowledge of AI, NLP, and some programming skillsRequires degrees in data science, statistics, or related fields, often with certifications in data analysis
Work EnvironmentOften project-based, working with AI models and large datasets in tech or research firmsUsually in corporate, research, or tech companies analyzing data to inform decisions
Industry UsageUsed in AI development, natural language processing, and machine learning projectsApplied across industries for data analysis, predictive modeling, and business insights

Temporary Retrieval Augmented Generation focuses on enhancing AI models with retrieval techniques, while Data Scientists analyze data to generate insights. Both roles require technical skills but serve different purposes within the tech and data ecosystem.

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Temporary Retrieval Augmented Generation jobs near you
Infographic showing various Temporary Retrieval Augmented Generation job openings in the United States as of May 2026, with employment types broken down into 65% Full Time, 29% Part Time, and 6% Temporary. Highlights an 94% Physical, 4% Hybrid, and 2% Remote job distribution, with an average salary of $43,381 per year, or $20.9 per hour.
Senior Software Engineer - Retrieval-Augmented Generation (RAG)

Senior Software Engineer - Retrieval-Augmented Generation (RAG)

RELX Group plc

Philadelphia, PA • On-site

$107.65K - $171.95K/yr

Other

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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