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Llm Ml Rag Jobs in Washington (NOW HIRING)

Kapitus

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

Traditional ML models * LLM APIs * RAG systems * Implement model monitoring frameworks for: * * Performance degradation * Drift detection * LLM output quality * Latency and token usage metrics

Commence

AI/ML Engineer

Washington, DC · On-site

$125K - $155K/yr

Support development of AI/LLM-driven workflows (e.g., document extraction, classification ... Exposure to vector databases, embeddings, or RAG components. * Experience with OCR/document AI ...

New

Commence

AI/ML Engineer

Washington, DC · On-site +1

$125K - $155K/yr

Support development of AI/LLM-driven workflows (e.g., document extraction, classification ... Exposure to vector databases, embeddings, or RAG components. * Experience with OCR/document AI ...

Commence

AI/ML Engineer

Washington, DC · On-site

$125K - $155K/yr

Support development of AI/LLM-driven workflows (e.g., document extraction, classification ... Exposure to vector databases, embeddings, or RAG components. * Experience with OCR/document AI ...

New

Finch AI

AI/ML Engineer - TS/SCI w/Polygraph

Mclean, VA · On-site

... RAG), semantic retrieval, LLM integration, or related AI workflows. • Strong proficiency in Python and modern AI/ML libraries, frameworks, and API integrations. • Active/current TS/SCI with ...

Assured Consulting Solutions

AI/ML Engineer

Reston, VA · On-site

... RAG), vector search, knowledge-grounded LLM approaches, or semantic search. • Experience with multi-model or ensemble approaches for improved performance or robustness. • Familiarity with ...

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Llm Ml Rag information

What is an llm ml rag job?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.

What are some typical challenges faced when working on retrieval-augmented generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What are the key skills and qualifications needed to thrive as an llm ml rag engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are popular job titles related to Llm Ml Rag jobs in Washington?

For Llm Ml Rag jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Llm Ml Rag jobs in Washington look for?

The top searched job categories for Llm Ml Rag jobs in Washington are:

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Cities in Washington with the most Llm Ml Rag job openings:

Llm Ml Rag jobs near you

AI/ML Data Specialist

Synectics for Management Decisions Inc

Washington, DC • On-site

$160K - $170K/yr

Full-time

Posted 17 days ago

Job description

Position Summary
We're looking for an AI/ML Data Specialist to design and deploy AI capabilities - including document ingestion, retrieval-augmented generation (RAG), and secure knowledge-base services - that improve a federal client's ability to discover, access, and analyze internal information, with guardrails for accuracy, reliability, and human oversight. Job location is the Washington, DC Metro Area. Hybrid or Remote options can be considered. Position is contingent upon contract award.
Key Responsibilities
  • Build automated document ingestion and indexing pipelines for unstructured content
  • Develop RAG capabilities for internal information retrieval, combining relevant-document retrieval with LLM-generated, citation-backed answers grounded strictly in internal content
  • Deploy secure AI-driven knowledge-base and query services appropriate for internal use
  • Define and implement evaluation metrics for accuracy, reliability, bias, and error rate
  • Establish AI guardrails, including monitoring, usage boundaries, and human oversight
  • Produce comprehensive documentation for models, data flows, interfaces, and evaluation results
  • Meet defined performance standards for accuracy, retrieval precision, and citation coverage

Required Qualifications
  • Experience building RAG or retrieval-based AI systems, including document ingestion, embedding, and indexing pipelines
  • Experience with LLM integration, prompt design, and evaluation of model accuracy, reliability, and bias
  • Familiarity with AWS-native AI/ML services and secure deployment of AI applications in a government environment
  • Understanding of AI governance and compliance requirements, including guardrails and human-oversight design
  • Strong documentation skills for model behavior, limitations, and user expectations

Preferred Certifications
  • AWS Certified Machine Learning - Specialty
  • AWS Certified Security - Specialty

Additional Requirements
Must be able to pass a federal background investigation prior to starting work and maintain eligibility throughout the engagement.
Salary range for this position is $160,000.00 - $170,000.00
Work Authorization: At this time, we are unable to sponsor new H-1B visas or other employment-based visa petitions for this position. Candidates must be authorized to work in the United States without current or future employer sponsorship.
Why Join Us?
Join a collaborative team supporting high-impact federal programs that incorporate innovative technology solutions. You'll have the opportunity to lead complex modernization initiatives, work alongside talented technical professionals, and help shape the future of government IT and data management.
Synectics is an equal opportunity employer.

Apply