Assist in the design, development, and deployment of AI-powered applications and prototypes using:
AWS Bedrock
Azure OpenAI
LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar frameworks
Develop and test prompt engineering strategies, retrieval-augmented generation (RAG) workflows, and AI agent capabilities.
Support integration of AI solutions with APIs, enterprise systems, and cloud services.
Build and maintain cloud-based environments in AWS and Azure.
Assist with infrastructure deployment using Infrastructure-as-Code tools such as Terraform, AWS CDK, or Bicep.
Support containerized deployments using ECS, EKS, or AKS.
Participate in CI/CD implementation and cloud automation efforts.
Support senior engineers in troubleshooting cloud, AI, and data integration challenges.
Assist in developing reusable templates, accelerators, and reference architectures.
Participate in rapid prototyping efforts that demonstrate mission value for federal customers.
Collaborate with Cloud, Cybersecurity, Data Intelligence, and Agile Engineering teams.
Contribute to technical documentation, architecture diagrams, and knowledge-sharing initiatives.
Stay current with emerging AI, machine learning, and cloud technologies.
Support implementation of solutions that align with:
NIST AI Risk Management Framework (AI RMF)
FedRAMP requirements
Zero Trust principles
Secure coding and responsible AI practices.

Bachelor's degree in Computer Science with a concentration, specialization, minor, or significant coursework in Artificial Intelligence, Machine Learning, Data Science, or a closely related field.
0–2 years of professional software engineering, AI/ML, or cloud development experience.
Relevant internships, research projects, capstone projects, graduate assistantships, or co-op experience may be substituted for professional experience.
Demonstrated programming experience in Python through coursework, projects, internships, or employment.
Foundational understanding of machine learning, generative AI, and large language models.
Experience using cloud platforms (AWS or Azure) through coursework, internships, certifications, or projects.
Familiarity with:
REST APIs
Git version control
Containerization concepts
Data pipelines and databases
Understanding of prompt engineering, RAG concepts, or AI agents through academic or personal projects.
Strong analytical and problem-solving skills.
One of the following is preferred within the first year of employment:
AWS Certified Cloud Practitioner or AWS Solutions Architect Associate
Microsoft Azure Fundamentals (AZ-900) or Azure AI Fundamentals (AI-900)

Federal AI Solutions Engineer – Entry Level

Key skills