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Remote Rag Jobs in Washington (NOW HIRING)

Elder Research

AI/ML Engineer

Washington, DC · Remote

$144K - $187K/yr

Implement and maintain RAG pipelines, including document processing, embedding generation ... For Remote Opportunities), education and certifications as well as Federal Government Contract ...

Elder Research

AI/ML Engineer

Washington, DC · Remote

$144K - $187K/yr

Implement and maintain RAG pipelines, including document processing, embedding generation ... For Remote Opportunities), education and certifications as well as Federal Government Contract ...

Elder Research

AI/ML Engineer

Washington, DC · Remote

$144K - $187K/yr

Implement and maintain RAG pipelines, including document processing, embedding generation ... For Remote Opportunities), education and certifications as well as Federal Government Contract ...

ITC Federal, Inc.

AI/ML Solutions Architect

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

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 ...

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Remote Rag information

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
Artificial Intelligence (AI) Engineer - Backend Focus

Artificial Intelligence (AI) Engineer - Backend Focus

ExpediteInfoTech Inc

Rockville, MD • Remote

Other

Posted 3 days ago

Job description

Job Title: Artificial Intelligence (AI) Engineer - Backend Focus
Client: Federal Government
Location: Remote with occasional travel to the client site in Baltimore. Candidates must currently live within a commutable distance of the office.
Employment Type: W2 on ExpediteInfoTech's payroll. This position requires a Permanent resident or a U.S. citizen. The selected candidate will go through a Public Trust Clearance process.
About ExpediteInfoTech: ExpediteInfoTech is a trusted federal contractor focused on leveraging emerging technologies to modernize systems, enhance security, and drive operational efficiency across government agencies. We work with clients across AI/ML, RPA, Cloud, Enterprise Architecture, Cybersecurity, Health IT infrastructure, and Federal Financial systems-all delivered through a hands-on, collaborative, results-driven approach. Our core values are collaborative innovation, quality service, and exceeding expectations. https://expediteinfotech.com/)
Position Summary: A backend-focused AI engineer responsible for developing secure, scalable, and production-grade AI applications, with deep experience in LLM integration, retrieval-augmented generation (RAG) pipelines, including Graph-RAG, Agentic AI, and cloud-based LLM Ops workflows. The role emphasizes Amazon SageMaker Studio, ECS, ECR, lambdas, Agentic Core, APIs, OpenSearch Vector DB, and Dynamo DB for operationalizing GenAI-powered Digital Products within FedRAMP-compliant AWS environments.
Responsibilities:
AI Solution Development:
  • Expert hands-on building of RAG and Graph-RAG architectures to handle multiple complex data formats (PDF, images, tables, Word documents, Excel, acronyms, attachments, etc.) to create cleansed standardized data for hydration into a vector database.
  • Expert hands-on knowledge on text embeddings, image embeddings, chunking logic, metadata creation, and embedding vectors indexing.
  • Expert hands-on knowledge in creating a highly accurate RAG retrieval system with knowledge on reranking, semantic search, similarity search, hybrid search, etc., to search by text or images.
  • Implement secure, scalable, highly accurate RAG, Agentic AI pipelines using LangChain, Strands, MCP, A2A frameworks, or AWS-native services like Bedrock, Agentic Core, OpenSearch Vector Database, and Knowledgebase.
  • Create backend infrastructure for chatbot applications with long-term and short-term memory capabilities to improve user experience.
  • Hands-on knowledge of creating APIs, Graph-RAG, develop agentic AI workflows with MCPs, A2A, and Skills.
AI/ML Skills:
  • Experience operationalizing AI/ML pipelines in SageMaker Studio with model governance
  • Experience with Amazon - Bedrock, Agentic Core, OpenSearch Vector Database, knowledgebase, lambda, API Gateway, FASTAPIs or Flask, SQS, SNS, Step functions, DynamoDB, RDS/Postgres SQL, ECS, ECR, IAM, CloudWatch, and EKS or Fargate.
  • Frameworks: LangChain, LangFuse, LlamaIndex, Strands, RAGAS, CrewAI, MCP, and A2A.
  • Prompt engineering, LLM evaluation methodologies, bias detection, and hallucination detection.
LLM Integration & LLM Ops:
  • Integrate multiple LLMs via APIs (AWS Bedrock: Anthropic - Claude, Titan, Llama, Stability Diffusion models)
  • Implement structured prompt engineering frameworks, response evaluation tools, and feedback loops
  • Build model optimization layers, including prompt selectors, model switchers, and cache layers
Cloud Infrastructure & Deployment:
  • Deploy AI services using SageMaker, ECS, Lambdas, Agentic Core, and Elastic Load Balancers
  • Containerize backend systems with Docker and deploy to scalable environments using ECS/EKS
  • Implement CI/CD pipelines via GitHub Actions integrated with AWS Systems Manager and CodePipeline
  • Architect solutions for VPC isolation, IAM hardening, and FedRAMP High compliance
System Integration & Maintenance:
  • Integrate AI workflows with enterprise databases, legacy platforms, and identity providers
  • Monitor service performance, GPU utilization, and system health via CloudWatch and custom logging
  • Build automated testing pipelines for model accuracy, bias detection, and system robustness
  • Maintain technical documentation and developer runbooks for long-term system support
Work Environment:
  • Remote-first with collaborative engagements and occasional client travel
  • Mission-focused development aligned with executive priorities
  • Continuous learning and rapid prototyping of cutting-edge AI technologies
  • Agile delivery culture with strong cross-functional collaboration
Required:
Required / Minimum Qualifications
  • 10+ years of IT experience.
  • 3+ years of experience as an AI Engineer
  • 3+ years of experience in AWS
  • AWS Services: Graph RAG, Bedrock Agentic Core, Agentic AI, EC2 (GPU-enabled), SageMaker (Studio, Pipelines, Endpoints, Model Registry), Bedrock, OpenSearch Vector DB, Systems Manager, Load Balancers, Amazon - Bedrock, OpenSearch Vector Database, knowledgebase, lambda, API Gateway, FASTAPIs or Flask, SQS, SNS, Step functions, DynamoDB, RDS/Postgres SQL, and EKS or Fargate
  • Proficient in coding: Python (async, FastAPI, LangChain, Transformers) and Terraform
  • DevSecOps: Docker, GitHub, GitHub Actions, CI/CD pipelines
  • Cloud-Native Development: Infrastructure-as-Code, cloud monitoring, and security policies
Preferred:
Preferred / Nice-to-Have Qualifications
  • Experience with React or other frontend frameworks for full-stack AI interfaces (Streamlit, ReactJS, JavaScript, Typescript, HTML, and CSS)
  • Government/federal sector AI solution experience with FedRAMP High or FISMA
  • Bachelor's or equivalent in Computer Science, Software Engineering, AI/ML, or related technical field
  • AWS certifications (Machine Learning Specialty, Solutions Architect) a strong plus
  • Experience using AI coding assistant tools like OpenAI Codex and Claude Code.

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