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Ai Rag Jobs in Virginia (NOW HIRING)

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Principal AI Engineer

Arlington, VA · On-site

Architect and build scalable, interoperable, responsible AI solutions, including generative AI, RAG, agentic AI, and predictive models, and guide their deployment into production. * Serve as a ...

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer (Python, AWS, GenAI) Location: Reston, VA (In-person interviews required) Candidate ... Architect and operationalize RAG pipelines , embeddings, vector databases, and LLM-powered ...

Steampunk

AI Developer

Mclean, VA

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi-agent workflows, RAG pipelines, and specialized AI microservices. * Implement reusable AI components ...

Closure Technologies

AI/ML Engineer

Mclean, VA

Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval ... Implement and maintain RAG pipelines, including document processing, embedding generation ...

Steampunk

AI Developer

Mclean, VA · On-site

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi-agent workflows, RAG pipelines, and specialized AI microservices. * Implement reusable AI components ...

Accenture Federal Services

AI Architect

Chantilly, VA · On-site

Must have experience designing and deploying LLM-based systems, including RAG, tool-calling, and multi-agent patterns, as well as a strong understanding of modern AI architectures such as embeddings ...

Steampunk

AI Developer

Mclean, VA · On-site

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi-agent workflows, RAG pipelines, and specialized AI microservices. * Implement reusable AI components ...

Steampunk

AI Developer

Mclean, VA · On-site

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi-agent workflows, RAG pipelines, and specialized AI microservices. * Implement reusable AI components ...

Amivero

AI Engineer

Reston, VA · On-site

Develop scalable Retrieval-Augmented Generation (RAG) architectures that improve response quality ... Support deployment of scalable and secure AI services using containers, serverless, and modern ...

Steampunk

AI Developer

Mclean, VA

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi-agent workflows, RAG pipelines, and specialized AI microservices. * Implement reusable AI components ...

Steampunk

AI Developer

Mclean, VA · On-site

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi-agent workflows, RAG pipelines, and specialized AI microservices. * Implement reusable AI components ...

GRVTY

AI/ML Engineer

Mclean, VA · On-site

They are seeking an experienced AI/ML Engineer to implement and maintain RAG pipelines, integrate LLMs into applications, and develop REST API interactions to support data retrieval and system ...

Amivero

AI Engineer

Reston, VA · On-site

$75K - $190K/yr

Develop scalable Retrieval-Augmented Generation (RAG) architectures that improve response quality ... Support deployment of scalable and secure AI services using containers, serverless, and modern ...

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Ai Rag information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer, AI research director, or executive roles like AI CTO. These roles often require advanced skills in data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

What are AI RAGs?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

What are some common challenges faced by AI RAG (Retrieval-Augmented Generation) engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
What are popular job titles related to Ai Rag jobs in Virginia? For Ai Rag jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Virginia look for? The top searched job categories for Ai Rag jobs in Virginia are:
What cities in Virginia are hiring for Ai Rag jobs? Cities in Virginia with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Virginia as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

AI/ML Engineer with Security Clearance

karan.kapadia@ivertix.com

Arlington, VA • On-site

Other

Posted 15 days ago

Job description

IVERTIX is hiring AI/ML Engineers for our client DOD . Kindly go through below JD and revert back with an updated resume. AI/ML Engineer Location: DC/MD/VA Preferred | Hybrid/Remote considered
Prime: Accenture Federal Services (AFS)
Clearance: U.S. Citizenship required; active Secret/TS/SCI preferred; ability to obtain and maintain clearance
Level: Junior / Journeyman / Senior Role Summary Design, build, and operate AI/ML solutions on the Advana data and analytics platform, including traditional ML and Generative AI/LLM capabilities. Work closely with data engineers, software engineers, and mission stakeholders to turn data into deployed, monitored models that support DoD decision-making. Core Responsibilities Develop, train, and evaluate ML models (classification, regression, clustering, NLP, time series).
Build MLOps pipelines for data prep, training, deployment, and monitoring on cloud infrastructure.
Integrate models into production services and user-facing applications (APIs, microservices, dashboards).
Implement and tune Generative AI/LLM solutions (e.g., RAG, prompt engineering, fine-tuning) using enterprise and mission data.
Collaborate with data engineers to define features, data quality checks, and scalable data pipelines.
Monitor model performance and drift; design retraining strategies and A/B tests.
Document models, assumptions, and limitations; support DoD Responsible/Ethical AI requirements.
Required Skills & Experience Strong Python development (pandas, NumPy, scikit-learn; plus TensorFlow and/or PyTorch).
Hands-on experience building and deploying ML models end-to-end (from data exploration to production).
Practical experience with MLOps tools and patterns (e.g., MLflow, SageMaker, Kubeflow, or similar).
Solid understanding of statistics, ML fundamentals, and evaluation metrics.
Experience working with cloud services (preferably AWS: S3, Lambda, ECR, ECS/EKS, SageMaker or equivalents).
Proficient with SQL and working with large structured/unstructured datasets.
Ability to work with cross-functional teams (data, software, product, mission).
Preferred Qualifications Experience with LLMs / Generative AI, RAG architectures, and vector databases.
Experience on large-scale data platforms (Databricks, Spark) and event/stream processing.
Experience in DoD, Federal, or other highly regulated environments (security, compliance, RMF awareness).
Familiarity with CI/CD, containerization (Docker), and Kubernetes for model deployment.
Relevant certifications (e.g., AWS Machine Learning Specialty, Databricks, or similar).

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