We are seeking a mid-level Applied AI Engineer with a strong focus on Agentic AI and Generative AI solutions. This role will support the design, development, and optimization of intelligent systems leveraging large language models (LLMs), agent frameworks, and cloud-based AI services.
The ideal candidate is hands-on, comfortable working across the AI development lifecycle, and experienced in building scalable, production-ready AI-driven applications. This is a fast-paced, consulting-oriented role supporting public sector initiatives.
Per our client contract, candidates must be U.S. Citizens and be able to obtain Public Trust Clearance
Responsibilities
- Design, develop, and deploy agent-based AI systems using modern LLM frameworks
- Implement and refine prompt engineering strategies for high-performing model outputs
- Build and maintain Generative AI solutions using platforms such as AWS Bedrock or similar
- Develop AI-driven applications and services using Python
- Optimize model performance through inference tuning and optimization techniques
- Conduct and support model fine-tuning and experimentation efforts
- Collaborate with cross-functional teams to translate business needs into AI-enabled solutions
- Ensure solutions meet performance, scalability, and security requirements in a public-sector environment
- Document technical designs, workflows, and implementation approaches
Qualifications
- 3–5+ years of hands-on experience in software development, AI/ML engineering, or related field
- Strong experience in:
- Generative AI (GenAI) development
- Prompt Engineering and LLM interaction patterns
- Agent-based system development (Agentic AI)
- Python programming
- Familiarity with AI/ML platforms such as AWS Bedrock (preferred), Strands, or equivalent
- Experience with:
- Model inference optimization
- Model fine-tuning techniques
- Strong problem-solving skills and ability to work independently in a consulting environment
Preferred Qualifications
- Experience working with public sector or government clients
- Familiarity with responsible AI practices and model governance
- Understanding of cloud-based AI architectures (AWS preferred)
- Exposure to CI/CD pipelines and MLOps practices
- Experience integrating AI capabilities into enterprise systems