Vienna, Virginia · USFull-time and Contractor1 month ago
About the role
Description
Lead or contribute to cross-functional teams to develop and operationalize AI solutions that help solve our most challenging problems. - Apply modern engineering techniques to design, develop, deploy and maintain end-to-end AI workflows spanning model training, inference and performance monitoring. - Adapt and integrate diverse AI model architectures, including computer vision systems, natural language processors, audio processors, large language models (LLMs) and multi-modal frameworks to address complex mission-critical challenges. - Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency and address these challenges through regular fine-tuning to ensure continued alignment with evolving mission needs and organizational goals. - Maintain knowledge of current AI research and adapt emerging techniques to intelligence applications. - Test and evaluate AI solutions against mission requirements and produce actionable recommendations.
Job responsibilities
• Design, develop, train, test, deploy, and maintain AI and machine learning models for enterprise-scale and Government applications.
• Develop and operationalize production of AI/ML solutions, ensuring models and supporting systems are reliable, scalable, maintainable, and appropriate for VA mission requirements.
• Collect, clean, transform, analyze, and prepare structured and unstructured data for AI/ML model development, training, testing, and evaluation.
• Evaluate potential AI technologies, models, tools, frameworks, and architectures and recommend solutions appropriate for complex Federal Government and cybersecurity use cases.
• Apply AI and machine learning techniques to solve real-world business, cybersecurity, engineering, analytics, and operational problems across a large enterprise environment.
• Develop and support Generative AI and Large Language Model (LLM) solutions, including model configuration, system prompts, classifiers, fine-tuning, content moderation, safety filters, and other model controls.
• Evaluate AI-generated outputs for accuracy, factuality, grounding, reliability, bias, helpfulness, honesty, and overall model performance before outputs are incorporated into VA efforts.
• Design and execute AI/ML model evaluation and validation methodologies, including benchmark testing and comparative evaluation of model outputs.
• Perform and support AI red-team testing to identify weaknesses, unintended model behaviors, bias, security vulnerabilities, and other risks associated with AI-generated outputs.
• Implement mechanisms and controls to improve the factuality and grounding of AI/LLM outputs, including system-level instructions, source attribution, content controls, and appropriate response behavior when information is incomplete, contradictory, or uncertain.
• Produce technical documentation, analysis, recommendations, briefings, and other materials that clearly communicate AI architecture, model performance, risks, controls, and recommended courses of action to both technical and non-technical stakeholders.
• Deliver Government-owned AI-related technical artifacts developed under the program, including applicable models, model weights, algorithms, configurations, documentation, data models, source code, and supporting technical components.
• Other responsibilities as assigned
Minimum qualifications
• Minimum of 5 years of experience in relevant fields including IT security, data science, or software engineering, design, development, or deployment of AI or machine learning systems in enterprise or Government environments
• Experience working on data science, machine learning, or AI projects, either through internships, research projects, or professional roles.
• Experience building, training, and deploying machine learning models and AI systems in a production environment.
• Experience collaborating with cross-functional teams, including data scientists, software developers, and domain experts.
• Experience in data collection, cleaning, and analysis techniques to prepare datasets for AI modeling.
• Experience applying AI solutions to real-world problems in various enterprises and domains such as large corporations and Government agencies similar in size/scope to GSA, IRS, DoD or VA
• Bachelor's degree in engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with software development using Python or similar coding languages.
• Experience architecting AI systems on cloud platforms (e.g., AWS, Google Cloud Platform).
Preferred qualifications
• Master’s or PhD in AI, Computer Science, or related technical field.
• Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
• Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
• Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
• Track record of troubleshooting live, high-traffic systems during critical windows.
Pay: $115,000.00 - $130,000.00 per year
Benefits:
• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Paid time off
• Retirement plan
• Vision insurance
Education:
• Bachelor's (Required)
Experience:
• AI: 4 years (Required)
• Data science: 4 years (Required)
• Machine learning: 3 years (Required)
• Python: 4 years (Required)
• AWS: 4 years (Required)
• RAG: 2 years (Preferred)
Work Location: In person
Lead or contribute to cross-functional teams to develop and operationalize AI solutions that help solve our most challenging problems. - Apply modern engineering techniques to design, develop, deploy and maintain end-to-end AI workflows spanning model training, inference and performance monitoring. - Adapt and integrate diverse AI model architectures, including computer vision systems, natural language processors, audio processors, large language models (LLMs) and multi-modal frameworks to address complex mission-critical challenges. - Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency and address these challenges through regular fine-tuning to ensure continued alignment with evolving mission needs and organizational goals. - Maintain knowledge of current AI research and adapt emerging techniques to intelligence applications. - Test and evaluate AI solutions against mission requirements and produce actionable recommendations.
Job responsibilities
• Design, develop, train, test, deploy, and maintain AI and machine learning models for enterprise-scale and Government applications.
• Develop and operationalize production of AI/ML solutions, ensuring models and supporting systems are reliable, scalable, maintainable, and appropriate for VA mission requirements.
• Collect, clean, transform, analyze, and prepare structured and unstructured data for AI/ML model development, training, testing, and evaluation.
• Evaluate potential AI technologies, models, tools, frameworks, and architectures and recommend solutions appropriate for complex Federal Government and cybersecurity use cases.
• Apply AI and machine learning techniques to solve real-world business, cybersecurity, engineering, analytics, and operational problems across a large enterprise environment.
• Develop and support Generative AI and Large Language Model (LLM) solutions, including model configuration, system prompts, classifiers, fine-tuning, content moderation, safety filters, and other model controls.
• Evaluate AI-generated outputs for accuracy, factuality, grounding, reliability, bias, helpfulness, honesty, and overall model performance before outputs are incorporated into VA efforts.
• Design and execute AI/ML model evaluation and validation methodologies, including benchmark testing and comparative evaluation of model outputs.
• Perform and support AI red-team testing to identify weaknesses, unintended model behaviors, bias, security vulnerabilities, and other risks associated with AI-generated outputs.
• Implement mechanisms and controls to improve the factuality and grounding of AI/LLM outputs, including system-level instructions, source attribution, content controls, and appropriate response behavior when information is incomplete, contradictory, or uncertain.
• Produce technical documentation, analysis, recommendations, briefings, and other materials that clearly communicate AI architecture, model performance, risks, controls, and recommended courses of action to both technical and non-technical stakeholders.
• Deliver Government-owned AI-related technical artifacts developed under the program, including applicable models, model weights, algorithms, configurations, documentation, data models, source code, and supporting technical components.
• Other responsibilities as assigned
Minimum qualifications
• Minimum of 5 years of experience in relevant fields including IT security, data science, or software engineering, design, development, or deployment of AI or machine learning systems in enterprise or Government environments
• Experience working on data science, machine learning, or AI projects, either through internships, research projects, or professional roles.
• Experience building, training, and deploying machine learning models and AI systems in a production environment.
• Experience collaborating with cross-functional teams, including data scientists, software developers, and domain experts.
• Experience in data collection, cleaning, and analysis techniques to prepare datasets for AI modeling.
• Experience applying AI solutions to real-world problems in various enterprises and domains such as large corporations and Government agencies similar in size/scope to GSA, IRS, DoD or VA
• Bachelor's degree in engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with software development using Python or similar coding languages.
• Experience architecting AI systems on cloud platforms (e.g., AWS, Google Cloud Platform).
Preferred qualifications
• Master’s or PhD in AI, Computer Science, or related technical field.
• Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
• Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
• Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
• Track record of troubleshooting live, high-traffic systems during critical windows.
Pay: $115,000.00 - $130,000.00 per year
Benefits:
• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Paid time off
• Retirement plan
• Vision insurance
Education:
• Bachelor's (Required)
Experience:
• AI: 4 years (Required)
• Data science: 4 years (Required)
• Machine learning: 3 years (Required)
• Python: 4 years (Required)
• AWS: 4 years (Required)
• RAG: 2 years (Preferred)
Work Location: In person
What you'll do
- Responsibilities are not added
What we're looking for
- Qualifications are not added