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Ai Model Jobs in Virginia (NOW HIRING)

Intrepidgs

AI/ML Model Engineer

Mclean, VA

Position contingent upon contract award As the AI/ML Model Engineer , you will develop machine learning models that optimize automation and generate data driven insights.The role involves testing ...

Intrepidgs

AI/ML Model Engineer

Mclean, VA · On-site

Position contingent upon contract award As the AI/ML Model Engineer , you will develop machine learning models that optimize automation and generate data driven insights.The role involves testing ...

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Ai Model information

What is an AI model?

AI models are computer programs designed to simulate human intelligence by learning patterns from data and making predictions or decisions based on that learning. These models can perform a variety of tasks, such as recognizing speech, translating languages, analyzing images, and generating text. AI models are created using machine learning algorithms and are trained on large datasets to improve their accuracy and performance. Popular examples include neural networks, decision trees, and support vector machines. The effectiveness of an AI model depends on the quality of the data, the chosen algorithm, and the training process.

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

To excel as an AI Model Developer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and typically a degree in computer science, data science, or a related field. Familiarity with ML frameworks like TensorFlow or PyTorch, cloud platforms, and relevant certifications such as TensorFlow Developer or AWS Machine Learning Specialty are valuable. Critical thinking, continuous learning, and effective collaboration with interdisciplinary teams are key soft skills for success. These competencies enable the creation of accurate, reliable AI models that can effectively solve complex real-world problems.

What are some common challenges faced by professionals working as AI model developers, and how can they address them?

Professionals working as AI Model developers often encounter challenges such as managing large and complex datasets, ensuring model accuracy, and addressing issues of bias in algorithms. They may also need to balance the trade-off between model performance and interpretability, especially when deploying models in production environments. To overcome these challenges, AI Model developers typically collaborate closely with data engineers, domain experts, and other stakeholders, regularly validate their models, and stay updated with the latest advancements in the field to adopt best practices.

What is the difference between Ai Model vs Data Scientist?

AspectAi ModelData Scientist
Required CredentialsKnowledge of machine learning, programming skills, sometimes certifications in AI/MLDegree in data science, statistics, computer science; certifications beneficial
Work EnvironmentFocus on developing, training, and deploying AI modelsData analysis, interpretation, and visualization; often collaborates with AI teams
Industry UsageUsed in AI development, automation, and predictive modelingApplied across industries for insights, reporting, and decision-making

While both roles involve working with data and algorithms, an Ai Model primarily focuses on creating and refining AI systems, whereas a Data Scientist analyzes data to generate insights and supports AI development. The roles often overlap but serve distinct functions within the data and AI ecosystem.

What is the easiest AI Model job to get into?

Entry-level AI model jobs often include roles such as data annotator or junior machine learning assistant, which typically require basic programming skills in Python and understanding of data labeling. These positions usually have lower experience requirements and may offer on-the-job training, making them accessible for beginners entering the AI field.

What are popular job titles related to Ai Model jobs in Virginia?

For Ai Model jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Ai Model jobs in Virginia look for?

The top searched job categories for Ai Model jobs in Virginia are:

What cities in Virginia are hiring for Ai Model jobs?

Cities in Virginia with the most Ai Model job openings:

Ai Model jobs near you
Infographic showing various Ai Model job openings in Virginia as of August 2026, with employment types broken down into 62% Full Time, 24% Part Time, and 14% Contract. Highlights an 93% In-person, and 7% Remote job distribution.

AI/ML Automation Engineer

Arlington, VA • Hybrid

Argo Cyber Systems
Network Security • 11 - 50 employees
Learn more about Argo Cyber Systemsexternal

Full-time

Posted 17 days ago

Job description

AI/ML Automation Engineer 

Location: Government Facility (Hybrid/Onsite)
Security Clearance: Active Secret Clearance Required (TS/SCI Preferred)
Customer: U.S. Department of Homeland Security (DHS)
Employment Type: Full-Time

About Argo Cyber

Argo Cyber Systems is a Service-Disabled Veteran-Owned Small Business (SDVOSB) delivering advanced cybersecurity engineering, artificial intelligence, cloud security, digital forensics, threat intelligence, and cyber modernization services to Federal agencies and critical infrastructure organizations.

We are seeking an experienced AI/ML Automation Engineer  to architect and implement intelligent automation capabilities that enhance cyber operations, malware analysis, digital forensics, and mission decision-making. This role combines machine learning engineering, generative AI, cloud-native development, and workflow automation to build scalable AI solutions supporting national cybersecurity missions.

Position Overview

The AI/ML Automation Engineer serves as the senior technical lead responsible for designing, developing, deploying, and optimizing AI-driven capabilities across cybersecurity operations.

You will collaborate with cyber analysts, data scientists, software engineers, cloud architects, and mission stakeholders to implement advanced machine learning models, Large Language Model (LLM) integrations, Retrieval-Augmented Generation (RAG) solutions, autonomous agent workflows, and intelligent automation supporting operational cyber missions.

This position offers the opportunity to shape next-generation AI capabilities used in support of incident response, malware analysis, threat intelligence, digital forensics, and cybersecurity modernization.

Primary ResponsibilitiesAI & Machine Learning Engineering
  • Design, develop, and deploy enterprise AI and machine learning solutions supporting cybersecurity operations.
  • Develop intelligent automation using LLMs, foundation models, and autonomous AI agents.
  • Build Retrieval-Augmented Generation (RAG) pipelines for secure knowledge retrieval.
  • Design prompt engineering strategies and optimize AI model performance.
  • Develop feature engineering, data preparation, and ML training pipelines.
  • Support model evaluation, tuning, validation, and continuous improvement.
Intelligent Automation
  • Design end-to-end automation workflows for cyber operations.
  • Automate malware collection, detonation, classification, and analysis.
  • Build AI-driven orchestration supporting incident response and digital forensics.
  • Develop APIs enabling secure data exchange between cyber platforms.
  • Automate repetitive analytical processes to improve operational efficiency.
Cloud AI Engineering
  • Design cloud-native AI solutions using AWS services.
  • Develop AI integrations utilizing Amazon Bedrock and foundation models.
  • Implement scalable ML pipelines using Databricks and cloud data platforms.
  • Deploy containerized AI workloads using Docker and Kubernetes.
  • Build CI/CD pipelines supporting rapid AI model deployment.
Data Engineering
  • Design and maintain scalable data ingestion pipelines.
  • Build data transformation, normalization, and enrichment workflows.
  • Develop streaming data integrations supporting AI workloads.
  • Optimize data quality, governance, and operational performance.
  • Support structured, semi-structured, and unstructured data processing.
Cybersecurity AI
  • Develop AI-enabled malware analysis capabilities.
  • Support AI-driven threat intelligence enrichment.
  • Build detection engineering automation.
  • Develop machine learning capabilities supporting digital forensics.
  • Assist cyber analysts by integrating AI decision-support capabilities into operational workflows.
Collaboration & Leadership
  • Provide technical leadership for AI modernization initiatives.
  • Mentor engineers on AI engineering best practices.
  • Collaborate with cybersecurity SMEs and software development teams.
  • Develop architecture documentation, design artifacts, and technical standards.
  • Present AI capabilities to executive leadership and government stakeholders.
Required Qualifications
  • U.S. Citizenship
  • Active Secret Security Clearance
  • Ability to obtain DHS Entry on Duty (EOD) Suitability
  • Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or related STEM field
  • Five (5) or more years of professional experience developing AI, ML, automation, or data engineering solutions
  • Strong Python development experience
  • Experience with Databricks AI Platform
  • Experience implementing machine learning pipelines
  • Experience integrating Large Language Models (LLMs)
  • Experience with Amazon Bedrock and AWS AI services
  • Experience building REST APIs and cloud-native applications
  • Experience with Git, CI/CD pipelines, and software development best practices
  • Strong analytical, problem-solving, and communication skills
Preferred Qualifications
  • Active TS/SCI Clearance
  • Experience supporting DHS, CISA, NSA, FBI, DoD, or Intelligence Community programs
  • Experience implementing Generative AI solutions
  • Experience developing autonomous AI agents
  • Experience building RAG architectures
  • Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar AI orchestration frameworks
  • Experience with vector databases (Pinecone, FAISS, ChromaDB, OpenSearch Vector Engine)
  • Experience supporting malware analysis, digital forensics, or threat intelligence
  • Experience implementing MLOps pipelines
  • Experience deploying AI into production cloud environments
Desired Technical SkillsArtificial Intelligence
  • Large Language Models (LLMs)
  • Generative AI
  • Retrieval-Augmented Generation (RAG)
  • Prompt Engineering
  • AI Agents
  • Agentic AI
  • Model Fine-Tuning
  • Reinforcement Learning
  • Natural Language Processing (NLP)
  • Embedding Models
Machine Learning
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • XGBoost
  • ML Pipelines
  • Feature Engineering
  • Model Evaluation
  • Model Deployment
Cloud
  • AWS
  • Amazon Bedrock
  • SageMaker
  • Lambda
  • ECS
  • EKS
  • S3
  • IAM
  • CloudWatch
  • Databricks
Data Engineering
  • Spark
  • Kafka
  • Kinesis
  • Airflow
  • Prefect
  • SQL
  • NoSQL
  • Data Lakes
  • ETL/ELT
  • Vector Databases
Software Engineering
  • Python
  • FastAPI
  • REST APIs
  • Docker
  • Kubernetes
  • Git
  • GitHub Actions
  • Jenkins
  • Terraform
Cybersecurity
  • Malware Analysis
  • Threat Intelligence
  • Incident Response
  • Digital Forensics
  • MITRE ATT&CK
  • Detection Engineering
  • SIEM Integration
  • Security Automation
Desired Certifications

One or more of the following certifications are highly desirable:

  • AWS Certified Machine Learning – Specialty
  • AWS Certified AI Practitioner
  • AWS Solutions Architect – Professional
  • Databricks Certified Machine Learning Professional
  • Microsoft Azure AI Engineer Associate
  • Google Professional Machine Learning Engineer
  • CSSLP
  • DoD 8140 IAT Level III
  • CISSP (Preferred)
  • GIAC Certified Forensic Analyst (GCFA) (Preferred)
What Success Looks Like

Within your first year you will:

  • Deploy secure AI capabilities supporting DHS cyber operations.
  • Develop production-ready LLM and RAG solutions.
  • Automate malware analysis and cybersecurity workflows.
  • Improve analyst productivity through intelligent automation.
  • Establish scalable AI engineering standards and MLOps practices.
  • Deliver measurable mission improvements through AI-enabled decision support.
Why Join Argo Cyber?
  • Design next-generation AI capabilities supporting national cybersecurity.
  • Work with cutting-edge Generative AI, LLMs, and autonomous agent technologies.
  • Collaborate with elite cybersecurity engineers, AI researchers, and Federal mission partners.
  • Influence AI modernization across critical government missions.
  • Competitive compensation, comprehensive benefits, technical training, and opportunities to shape the future of AI-driven cyber operations.

Background & Drug Screening Disclaimer

© Argo Cyber Systems, LLC – All Rights Reserved

Argo Cyber Systems, LLC is committed to maintaining a safe, secure, and trusted workplace for all employees and our federal clients. Employment with Argo Cyber Systems is contingent upon successful completion of all required background investigations and pre-employment screenings, which may include, but are not limited to:

  • Criminal background checks (federal, state, and local)

  • Employment and education verification

  • Reference checks

  • Drug screening (in compliance with federal and state law)

  • Security clearance verification (as applicable for classified positions)

Candidates selected for employment in positions requiring access to sensitive or classified information may also be subject to additional U.S. Government background investigations and security adjudication processes, including DHS Entry on Duty (EOD) suitability or equivalent federal clearance requirements.

Argo Cyber Systems reserves the right to disqualify or rescind an offer of employment based on the results of any background or screening process that, in the company's judgment, may impact an individual's ability to perform essential job functions or meet contractual obligations.

All background investigations and screenings are conducted in accordance with applicable federal, state, and local laws, including the Fair Credit Reporting Act (FCRA). Candidates will be notified of their rights and provided an opportunity to review and dispute any adverse findings before final employment determinations are made.

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