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Summer 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 ...

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 ...

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 ...

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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Summer Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Retrieval Augmented Generation (RAG) Engineer, and why are they important?

To thrive as a Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, typically supported by a degree in computer science or a related field. Proficiency with frameworks like PyTorch or TensorFlow, experience with vector databases (e.g., FAISS, Pinecone), and familiarity with LLM APIs are commonly required. Creative problem-solving, strong communication, and the ability to collaborate across multidisciplinary teams are essential soft skills. These competencies ensure effective development, deployment, and optimization of advanced AI systems that integrate retrieval and generative capabilities.

What is a Summer Retrieval Augmented Generation role?

A Summer Retrieval Augmented Generation (RAG) role typically refers to a summer position focused on developing or improving retrieval-augmented generation systems, which are AI models that combine information retrieval with generative capabilities. In this role, you might work on integrating search algorithms with large language models, enabling systems to fetch relevant information from external sources and generate accurate, context-aware responses. These positions are often found in research labs, tech companies, or startups working on advanced AI applications, and are ideal for students or early-career professionals interested in machine learning, natural language processing, and AI research.

What are some common challenges faced when working on Retrieval-Augmented Generation (RAG) projects during a summer internship?

During a summer internship focused on Retrieval-Augmented Generation (RAG), interns often encounter challenges such as integrating retrieval systems with generative models, managing large-scale datasets, and optimizing latency for real-time responses. Collaboration with cross-functional teams—including data engineers, research scientists, and product managers—is essential for aligning project goals and troubleshooting implementation issues. Additionally, interns may need to balance exploratory research with delivering usable prototypes within tight timeframes, which helps develop both technical and project management skills.
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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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