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Remote Rag Jobs in Ashburn, VA (NOW HIRING)

ITC Federal, Inc.

Full Stack Developer - AI/ML

Fairfax, VA · On-site +1

$180K - $220K/yr

Hybrid - 3 days onsite/2 remote POSITION TIMING: ASAP; hiring immediately BENEFITS: Health, Dental ... Build and implement Retrieval-Augmented Generation (RAG) pipelines and AI-powered applications

NuAxis Innovations

Senior AI Developer

Washington, DC · On-site +1

$61.75 - $81.50/hr

We are currently seeking a talented and motivated Senior AI Developer for a remote federal program ... Design and implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge ...

Northramp LLC

AI Engineer (Mid)

Washington, DC · On-site +1

This role is remote with a preference for candidates located in Virginia, Maryland, or Washington ... Integrate AI/ML capabilities (Vertex AI, Gemini APIs, embeddings, RAG) into enterprise Java and ...

Northramp LLC

AI Engineer (Mid)

Washington, DC · Remote

This role is remote with a preference for candidates located in Virginia, Maryland, or Washington ... Integrate AI/ML capabilities (Vertex AI, Gemini APIs, embeddings, RAG) into enterprise Java and ...

Northramp LLC

AI Engineer (Mid)

Washington, DC · Remote

This role is remote with a preference for candidates located in Virginia, Maryland, or Washington ... Integrate AI/ML capabilities (Vertex AI, Gemini APIs, embeddings, RAG) into enterprise Java and ...

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How much do remote rag jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for remote rag in Ashburn, VA is $21.99, according to ZipRecruiter salary data. Most workers in this role earn between $18.41 and $23.37 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Rag, and why are they important?

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What is a Remote RAG (Retrieval-Augmented Generation) specialist?

A Remote RAG specialist is a professional who works with Retrieval-Augmented Generation (RAG) systems, typically in the field of artificial intelligence and machine learning. RAG combines traditional information retrieval techniques with generative models like large language models to provide more accurate and contextually relevant answers to user queries. Remote RAG specialists often build, fine-tune, and maintain these systems while working from a remote location. They may also work on integrating RAG models into applications, improving retrieval accuracy, and customizing outputs based on user needs.

What are some common challenges faced by professionals working in a remote RAG (Responsible AI Governance) role?

Professionals in remote RAG roles often encounter challenges related to cross-functional collaboration and maintaining clear communication, especially when working across different time zones. Ensuring alignment on ethical AI standards and compliance requirements can be complex, as it typically involves coordinating with data scientists, legal teams, and business stakeholders. Staying current with evolving regulatory frameworks and best practices in AI governance is also essential, demanding continuous learning and adaptability. Building trust and rapport within a remote team can require extra effort, but leveraging digital collaboration tools and regular check-ins can help mitigate these challenges.
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Remote Rag jobs near you

Gen AI / Agentic Engineer

Interon IT Solutions

Chantilly, VA • Remote

Contractor

Posted 19 days ago

Job description

#W2 Role

Job Title: Gen AI / Agentic Engineer

Location: Remote 
Type: W2 Contract 
Experience: 10+ years overall IT, 2+ years GenAI/LLM

Job Summary

We are looking for a GenAI / Agentic Engineer to design, build, and deploy LLM-powered applications on AWS. This role is focused on real production engineering—APIs, RAG pipelines, agent workflows, evaluation, deployment, monitoring, and performance/cost tuning.

Responsibilities

  • Build and maintain LLM-powered backend services using Python and FastAPI (chat, search, summarization, Q&A).
  • Design and implement RAG pipelines end-to-end: ingestion, parsing, chunking, embeddings, indexing, retrieval, reranking, and grounded responses.
  • Develop agentic workflows for multi-step automation (tool calling, orchestration, state/memory, retries, audit logs).
  • Deploy and support GenAI workloads on AWS using ECS/Lambda, S3, SQS, DynamoDB/RDS, OpenSearch (or vector store), and related services.
  • Implement security and governance controls: auth, authorization, secrets, encryption, PII handling, and prompt-injection defenses.
  • Build evaluation and monitoring for quality, hallucination reduction, latency, and cost (test sets, regression checks, dashboards, alerts).
  • Work across full SDLC: design docs, estimates, coding, code reviews, CI/CD, testing, release, and production support.
  • Communicate architecture decisions clearly and explain tradeoffs (accuracy vs latency vs cost) to stakeholders.

Required Skills (Point-Based)

  • 10+ years overall IT experience with backend/API engineering and cloud deployments
  • 2+ years hands-on GenAI/LLM experience delivering real features (not just demos)
  • 6+ years strong Python (core Python, clean coding, debugging, packaging)
  • Experience with asyncio and concurrency (threads/async), plus profiling and performance tuning
  • Comfortable with stateful/long-running workflows: transaction handling, retries, idempotency, and failure recovery
  • 5+ years building REST APIs / microservices, strong API design and error handling
  • 5+ years with FastAPI (or similar) including middleware, dependency injection, background tasks
  • Experience implementing auth/security using JWT/OAuth, RBAC, secure configuration, secrets handling
  • Strong testing discipline using pytest (unit/integration tests, mocks, API contract testing)
  • Proven experience building RAG systems end-to-end: chunking strategies, embeddings, retrieval tuning, reranking, grounding/citations
  • Hands-on with RAG optimization: hybrid retrieval, metadata filters, top-k tuning, chunk tuning, reranking strategies
  • Experience with agentic patterns: tool calling, orchestration, memory/state, structured outputs, audit trails
  • Experience implementing guardrails: output schema enforcement (JSON), refusal handling, safety filters, prompt-injection defenses, PII masking
  • 5+ years AWS experience using ECS/Lambda, S3, SQS, DynamoDB/RDS (and related services)
  • Strong AWS security fundamentals: IAM, KMS, Secrets Manager, CloudWatch logs/metrics/alarms
  • Experience deploying LLM workloads via Amazon Bedrock (preferred) or SageMaker
  • Strong system design: scalability, caching, rate limiting, queues, resilience/failure handling
  • Ability to clearly explain GenAI architecture decisions and tradeoffs across accuracy/latency/cost

Nice to Have

  • LangChain / LangGraph / LlamaIndex (any)
  • OpenSearch vector search or vector DB experience (Pinecone/Weaviate/FAISS, etc.)
  • Docker, Terraform/CDK, CI/CD (GitHub Actions/Jenkins)
  • Experience in regulated environments (finance/healthcare/telecom) with governance controls