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Remote Retrieval Augmented Generation Jobs in Dallas, TX

Select Minds LLC

Sr Data Scientist GenAI

Dallas, TX · On-site +1

$150K - $210K/yr

... Retrieval-Augmented Generation). - Collaborate closely with ML Engineers, MLOps, software ... This position is temporarily remote. Compensation: $150,000.00 - $210,000.00 per year About Us We ...

Tiger Analytics Inc.

Lead Data Engineer - AWS

Dallas, TX · Remote

$104K - $138K/yr

Implement and optimize vector search capabilities using Amazon OpenSearch Serverless or specialized vector engines for RAG (Retrieval-Augmented Generation). * Serverless Data Engineering: Build ...

Tiger Analytics Inc.

Lead Data Engineer - AWS

Dallas, TX · On-site +1

$101K - $133K/yr

Implement and optimize vector search capabilities using Amazon OpenSearch Serverless or specialized vector engines for RAG (Retrieval-Augmented Generation). * Serverless Data Engineering: Build ...

Performacentric

Full Stack Developer

Dallas, TX · On-site +1

Remote (Preferred: Philippines, Latin America, or North America) Employment Type: Full-Time / ... Build retrieval-augmented generation (RAG) solutions. * Support prompt engineering and AI workflow ...

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Remote Retrieval Augmented Generation information

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

To thrive as a Remote Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and experience with retrieval systems such as Elasticsearch or FAISS are typically required. Problem-solving, effective communication, and adaptability are important soft skills for collaborating remotely and iterating on rapidly evolving AI solutions. These skills ensure the engineer can design, deploy, and optimize robust RAG systems that effectively combine retrieval and generation for high-quality AI outputs.

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

AspectRemote Retrieval Augmented GenerationRemote Data Scientist
CredentialsAI/ML knowledge, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development, NLP projectsData analysis, model building
Industry UsageAI, NLP, machine learningTech, finance, healthcare
Search & ComparisonOften compared for AI roles involving language modelsCompared for data analysis roles

Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.

What are some common challenges faced by professionals working in Remote Retrieval Augmented Generation roles, and how can they be addressed?

Professionals in Remote Retrieval Augmented Generation (RAG) roles often encounter challenges related to integrating diverse data sources, ensuring low latency in information retrieval, and maintaining the quality and relevance of augmented outputs. Coordinating effectively with distributed teams and adapting to rapidly evolving AI technologies are also common hurdles. To address these, staying current with best practices in data engineering, leveraging robust APIs, and participating in regular team check-ins can help ensure smooth collaboration and system performance.

What is Remote Retrieval Augmented Generation?

Remote Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models with external information sources. In a remote RAG setup, the model retrieves relevant data from remote databases or APIs during the generation process, enhancing its responses with up-to-date or domain-specific knowledge. This approach is widely used in applications that require accurate, context-aware answers, such as chatbots, search engines, and virtual assistants. By leveraging remote retrieval, RAG systems can access a broader range of information without needing to store all data locally.
What are popular job titles related to Remote Retrieval Augmented Generation jobs in Dallas, TX? For Remote Retrieval Augmented Generation jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Remote Retrieval Augmented Generation jobs in Dallas, TX look for? The top searched job categories for Remote Retrieval Augmented Generation jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Remote Retrieval Augmented Generation jobs? Cities near Dallas, TX with the most Remote Retrieval Augmented Generation job openings:
Remote Retrieval Augmented Generation jobs near you

Senior AI Application Engineer (Remote Opportunity)

VetsEZ

Dallas, TX • Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 10 days ago

Job description

VetsEZ is seeking a Senior AI Application Engineer to design, develop, and implement enterprise Artificial Intelligence (AI) solutions supporting the Department of Veterans Affairs (VA). The initial assignment will support the Joint Longitudinal Viewer (JLV) AI Summarization initiative, delivering a secure, governed AI-assisted search and summarization capability within an approved test environment utilizing Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Amazon Bedrock, while supporting clinical evaluation and future production readiness.

Working closely with AI Solution Architects, Product Owners, cybersecurity teams, DevSecOps engineers, and clinical stakeholders, this individual will develop secure, scalable, and maintainable AI-powered applications that integrate seamlessly with existing enterprise healthcare systems while ensuring compliance with Federal security, privacy, and AI governance requirements.

Responsibilities:

  • Design, develop, and implement enterprise AI applications utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
  • Develop AI-assisted search, summarization, and question-answering capabilities using Amazon Bedrock.
  • Build reusable AI services supporting prompt orchestration, document retrieval, and response generation.
  • Implement prompt engineering and source-grounding strategies to improve AI accuracy, consistency, traceability, and clinical relevance, including source links that support human verification.
  • Optimize AI performance while balancing response quality, latency, and operational cost.
  • Design and develop secure, scalable cloud-native applications utilizing modern software engineering practices.
  • Develop RESTful APIs and backend services supporting AI capabilities and enterprise integrations.
  • Implement approved document retrieval, vector search, and semantic search capabilities within the selected patient context and CHSD document set.
  • Develop automated unit, integration, and functional tests and repeatable AI evaluations for groundedness, retrieval quality, and clinical relevance supporting AI-enabled applications.
  • Troubleshoot software defects, optimize application performance, and support activities within the approved test environment and for future production readiness.
  • Integrate AI capabilities into existing enterprise healthcare applications and clinical workflows.
  • Develop secure interfaces utilizing REST APIs and modern integration patterns.
  • Support interoperability utilizing healthcare standards including FHIR, HL7, and CCD.
  • Collaborate with Solution Architects and engineering teams to implement scalable and maintainable application designs.
  • Participate in code reviews and promote software engineering best practices across the development team.
  • Develop secure software in accordance with Federal cybersecurity and privacy requirements, including approved data-retention and purge controls.
  • Support CI/CD pipelines, automated deployments, and cloud-native operational practices.
  • Implement logging, monitoring, audit capabilities, and operational telemetry, including model and prompt version tracking, usage and cost monitoring, and controls to detect model, prompt, retrieval, and data drift.
  • Support application security scanning, vulnerability remediation, and activities within the approved test environment and for future production readiness.
  • Incorporate Responsible AI, Human-in-the-Loop (HITL), and AI governance principles into application development.
  • Collaborate with architects, product owners, clinicians, cybersecurity teams, and Government stakeholders throughout the software development lifecycle.
  • Participate in Agile ceremonies including Sprint Planning, backlog refinement, Sprint Reviews, and Retrospectives.
  • Contribute to technical documentation, implementation guides, and software design artifacts.
  • Present technical solutions and implementation approaches to project leadership and stakeholders.

Requirements:

  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Artificial Intelligence, Data Science, or a related technical field, or equivalent experience.
  • 8+ years developing enterprise software applications.
  • 5+ years developing cloud-native applications utilizing AWS or comparable cloud platforms.
  • Demonstrated experience developing Artificial Intelligence, Machine Learning, or Generative AI solutions.
  • Experience implementing applications utilizing Amazon Bedrock or similar enterprise AI platforms.
  • Experience developing enterprise REST APIs and cloud-native application services.
  • Amazon Bedrock and AWS cloud services
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Prompt engineering and AI evaluation
  • Python, Java, or C#
  • REST APIs and JSON
  • Vector databases, embeddings, and semantic search
  • Git, CI/CD, and DevSecOps
  • Healthcare interoperability (FHIR, HL7, CCD)

Additional Qualifications:

  • Strong understanding of modern software engineering principles and cloud-native application development.
  • Experience developing scalable, secure, and maintainable enterprise applications.
  • Excellent analytical, troubleshooting, and problem-solving skills.
  • Strong written and verbal communication skills with the ability to collaborate across multidisciplinary engineering teams.
  • Ability to obtain and maintain a Government Public Trust clearance.
  • Experience supporting the Department of Veterans Affairs (VA), Department of Defense (DoD), or other Federal healthcare organizations.
  • Experience developing AI-enabled clinical workflow, information-retrieval, or clinician-support applications requiring human validation.
  • Experience implementing vector search, embeddings, semantic search, and prompt orchestration.
  • Knowledge of Responsible AI, NIST AI Risk Management Framework (AI RMF), NIST SP 800-53, FISMA, and FedRAMP.
  • Familiarity with clinical terminology standards including SNOMED CT, ICD-10, RxNorm, and LOINC.
  • AWS Developer, AWS AI, Machine Learning, or other AWS cloud certifications are highly desirable.

Benefits:

  • Medical, Dental, and Vision Insurance
  • 401(k) with Employer Match
  • Paid Time Off plus Federal Holidays
  • Corporate Laptop
  • Professional Development and Training Opportunities
  • Remote Opportunity

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status.

Sorry, we are unable to offer sponsorship at this time.

Employment Type: FULL_TIME

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