Federal AI Solutions Engineer

• 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

• Academic or project experience with:
• LangChain
• LlamaIndex
• Semantic Kernel
• CrewAI
• Exposure to vector databases such as Pinecone, Weaviate, FAISS, or Milvus.
• Experience building AI, machine learning, or cloud-based capstone projects.
• Familiarity with DevOps, MLOps, or CI/CD concepts.
• Interest in federal government missions and emerging AI technologies.

• 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)

Salary will be determined based on education, certifications, internship experience, technical proficiency, and security clearance eligibility.