Agentic AI Engineer at Whitespace

Setup: Remote
Location: Alexandria, Virginia
Type: Full-time
Posted: Apr 14, 2026

Description

Whitespace is looking for an Agentic AI Engineer to join their team. This role involves creating AI models that go beyond simple text generation, focusing instead on building intelligent agents capable of taking action in complex technical environments. The engineer will contribute to designing and implementing autonomous AI agents for cybersecurity applications, collaborating with various teams, mentoring others, and continuously improving agentic systems.

Required Skills

Python
FastAPI
asyncio
PyTorch
Agent Frameworks
Generative AI Models
Prompt Engineering
LLM Models
Agentic Frameworks
Retrieval-Augmented Generation
Knowledge Graphs
MLOps Principles
Model Deployment
Monitoring
Cloud Computing Platforms

Additional Information

Minimum Education: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
Required Previous Experiences: At least 5 years of related experience.
Proficiency in Python, with experience in relevant libraries, frameworks, and technologies such as FastAPI, asyncio, and PyTorch.
Experience developing in Python and deploying robust AI/ML solutions, specifically in autonomous agents.
Experience integrating generative AI models (OpenAI, Anthropic, Google Gemini, LLaMA) into practical agentic systems.
Ability to design scalable, distributed systems and cloud-native architectures.
Extensive experience in prompt engineering and fine-tuning LLM models.
Ability to design and implement agentic frameworks and architectures.
Knowledge of techniques for grounding generative models with external knowledge sources (e.g., retrieval-augmented generation, Knowledge graphs).
Familiarity with MLOps principles and tools for model deployment and monitoring.
Understanding of the security implications and mitigation strategies for generative AI and agentic systems.
Preferred Qualifications: Familiarity with Cloud Computing Platforms.
Experience in deploying AI models at scale in high-throughput, low-latency environments.