Federal AI Solutions Engineer; Entry Level

Federal AI Solutions Engineer (Entry Level)

Hybrid - McLean, VA, United States

Empower, Innovate, Impact! At Team A-TEK, we empower people to drive innovation that impacts mission!

A-TEK operates at the intersection of mission and innovation by applying our deep domain expertise across the federal markets. Embracing our digital-first strategy, A-TEK provides enhanced capabilities in application development, digital transformation, enterprise IT, and scientific services. Our solutions are designed to modernize, automate, secure, protect, and enhance the operations of our federal clients, ensuring they stay ahead in a rapidly evolving digital landscape.

Our work is fueled by a passion to serve our clients’ needs and to protect the safety and welfare of Americans. That passion shapes how we nurture our most valuable asset – Our Employees. A-TEK actively cultivates the talent that drives our success and fosters a creative, challenging, and mission-driven work environment for current and future employees.

A-TEK is seeking an early-career Federal AI Solutions Engineer to support the design, development, and deployment of mission-focused AI and cloud solutions across AWS and Azure environments. This role is ideal for recent graduates with a strong academic foundation in Artificial Intelligence, Machine Learning, software engineering, and cloud technologies who are eager to apply their skills to real-world federal mission challenges.

The engineer will work alongside senior architects and engineers to develop AI-enabled applications, cloud-native solutions, automation workflows, and rapid prototypes that support A-TEK's Digital Shift innovation initiatives.

This is a hybrid role based in McLean, VA. No visa sponsorship is available for this role. This role requires the ability to obtain and retain a public trust level security clearance.

• Assist in the design, development, and deployment of AI-powered applications and prototypes using:
• AWS Bedrock
• Lang Chain, Llama Index, 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:
• 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
• Containerization concepts
• Data pipelines and…