Agentic AI Software Engineer

Riverside Research Institute•Fair Oaks, VA

63d•$115,000 - $200,000

About The Position

As an Mid Level Agentic AI Software Engineer, you will be responsible for designing, developing, and implementing agentic AI systems that can operate autonomously in complex environments. You will work closely with cross-functional teams to ensure that our AI systems are robust, scalable, and aligned with our organizational goals. Your role will involve both theoretical and practical aspects of AI, making it essential to stay updated with the latest developments in the field.

Requirements

A minimum of 8 years of related experience with a Bachelor's degree, 6 years with a Master's degree or 3 years and a PhD in Computer Science, Engineering, AI, or a related field.
Proven experience in AI development, particularly with agent-based systems and machine learning frameworks and A2A and MCP standards.
Strong programming skills in languages such as Python, Java, or C++.
Familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch) and tools for system simulation and modeling.
Excellent problem-solving skills and the ability to work independently and collaboratively.
Strong communication skills, both written and verbal
Active Secret clearance and ability to obtain Top Secret Security Clearance with SCI and ability to obtain necessary program clearances

Nice To Haves

Experience with multi-agent systems and game theory.
Knowledge of ethical AI frameworks and regulations.
Familiarity with cloud computing platforms (e.g., AWS, Azure) and DevOps practices.
Active TS/SCI

Responsibilities

System Design and Development:
Design and develop agentic AI systems with a focus on autonomy, efficiency, and ethical considerations.
Collaborate with data scientists and software developers to create scalable AI algorithms and architectures.
Research and Innovation:
Conduct research on the latest advancements in AI, particularly in agent-based systems, reinforcement learning, and decision-making frameworks.
Propose innovative solutions to challenging problems in the domain of agentic AI.
Testing and Evaluation:
Develop and implement testing protocols to evaluate the performance and reliability of AI systems.
Analyze system behavior and make recommendations for improvements based on empirical data.
Collaboration and Communication:
Work closely with stakeholders, including product managers, to understand requirements and translate them into technical specifications.
Present findings and project updates to technical and non-technical audiences.
Ethical Considerations:
Ensure that all AI systems comply with ethical guidelines and regulations, focusing on transparency, accountability, and user safety.
Engage in discussions regarding the societal impacts of agentic AI technologies.