MLOps Engineer

At Cynnovative, we leverage machine learning, computer science, and software engineering to address high-impact problems in the cyber domain, specifically those which are critical to U.S. national security. We primarily extend fundamental research to invent, design, develop, and deploy prototype solutions that support persistent problems in this domain.

As an MLOps Engineer at Cynnovative, you will be responsible for developing and managing tools that facilitate AI experimentation and deployments. This role is crucial in ensuring seamless integration and operation of machine learning models in various environments, supporting U.S. national security efforts.

NOTE: This role requires an active TS/SCI security clearance and is located on-site in Northern Virginia.

Design and build AI/ML experiment workflows and support systems (e.g., MLFlow, HPC).

• Implement reliable AI/ML test harnesses for deployment in secure environments.
• Implement containerization strategies (e.g., Docker, Kubeflow, Kubernetes) to streamline AI/ML training workflows.

Lead creation of orchestration systems for AI agents.

• Ensure efficient resource allocation and task scheduling across local and third party inference providers.
• Integrate with cloud providers (e.g., AWS, GCP, Azure) to scale for commercial use cases.

• B.S. in Computer Science, Software Engineering, or related field (M.S. or Ph.D. preferred for senior positions)
• Strong communication skills and willingness to collaborate in a cross-functional team environment with domain experts
• Knowledge of algorithms underlying LLMs, such as transformers, attention mechanisms, and sequence-to-sequence models
• Hands-on experience in deep learning frameworks (e.g., PyTorch, Tensorflow, Huggingface transformers, etc.)
• Solid understanding of fundamental ML algorithms, especially multimodal LLMs
• Experience using or designing CI/CD workflows
• Experience with version control systems, particularly Git
• Familiarity with open source LLMs (e.g., LLama, Qwen, DeepSeek)
• Proficiency in programming languages such as Python, Java, or C++
• U.S. Citizenship and active TS/SCI security clearance

• Keep up to date with the latest AI research
• Familiarity with LLM orchestration and agent frameworks (e.g., LangChain, CrewAI, LLamaIndex)
• Experience productionalizing ML systems through e.g., containerization (Docker) and orchestration (Kubernetes)
• Experience with a major cloud provider (e.g., AWS, GCP, Azure)