AI Engineering Support SME

Peraton•Washington, DC

1d•$135,000 - $216,000

About The Position

Peraton is seeking experienced AI professionals to support the modernization and expansion of enterprise Artificial Intelligence (AI) capabilities for a large federal customer. This effort will support strategic AI initiatives focused on automation, analytics, generative AI, intelligent agents, software engineering, governance, and enterprise modernization efforts. The selected candidate(s) will support the design, development, implementation, integration, governance, optimization, and sustainment of AI-enabled solutions across multiple business and technical domains. These efforts will support enterprise modernization objectives while maintaining responsible AI practices, transparency, accountability, operational sustainability, and mission alignment.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Physics, Artificial Intelligence, Information Technology, or related technical field with 16+ years experience or Master's degree with 14+ years related experience; additional experience may be considered in lieu of degree
U.S. Citizenship
Must have the ability to obtain and maintain a Public Trust clearance.
Candidates must have resided in the United States for the past five (5) years with no more than 6 months outside travel of the U.S. during this time frame.
Experience supporting enterprise AI, machine learning, automation, analytics, or software engineering initiatives.
Knowledge of generative AI, LLMs, AI orchestration frameworks, intelligent automation, and AI-enabled software development.
Experience with AI/ML frameworks and tools such as PyTorch, TensorFlow, LangChain, AutoGen, or similar technologies.
Experience with APIs, microservices, cloud technologies, CI/CD pipelines, and containerization technologies such as Kubernetes.
Strong understanding of AI governance, cybersecurity, data security, compliance, and ethical AI principles.
Ability to analyze complex technical challenges and provide scalable AI solutions.

Nice To Haves

Experience supporting federal government or enterprise modernization programs.
Experience with predictive analytics, data engineering, visualization, and dashboard development.
Experience supporting enterprise AI operational sustainment and lifecycle management activities.
Familiarity with vector databases, intelligent agents, and advanced AI orchestration capabilities.
Experience supporting AI strategic advisory or roadmap development initiatives.
Candidates with strong mathematical or computational backgrounds, including Mathematicians, are highly desired.
Relevant AI, cloud, cybersecurity, or engineering certifications are a plus.

Responsibilities

Support enterprise AI modernization initiatives aligned with customer operational and business objectives.
Design, develop, and implement AI-enabled solutions using machine learning, large language models (LLMs), generative AI, and intelligent automation technologies.
Develop autonomous and semi-autonomous AI agents capable of reasoning, planning, and executing multi-step workflows.
Build agentic AI architectures utilizing frameworks such as LangChain, AutoGen, and similar orchestration technologies.
Develop APIs, integrations, vector database solutions, and scalable AI services to support enterprise AI operations.
Implement MLOps practices, CI/CD pipelines, containerization technologies, and cloud-based deployment strategies.
Integrate AI capabilities into enterprise applications using APIs, microservices, and scalable cloud architectures.
Support AI model training, evaluation, deployment, optimization, troubleshooting, and operational sustainment activities.
Develop dashboards, reporting solutions, predictive analytics capabilities, and executive-level visualizations.
Support AI governance, security, compliance, ethical AI, transparency, and risk mitigation initiatives.
Collaborate with executive leadership, CIO organizations, technical teams, and business stakeholders to support AI adoption and modernization efforts.
Provide technical documentation, workshops, mentoring, training, and knowledge transfer activities.
Support ongoing operational monitoring, maintenance, retraining, optimization, and lifecycle management of deployed AI capabilities.
Identify opportunities for AI-enabled automation, operational efficiencies, productivity improvements, and enterprise modernization initiatives.
Translate technical AI concepts into business-focused recommendations and operational outcomes.