AI/ML Engineer – AI Foundations Group

At Dream, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; it’s a Dream job. Dream is where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Let’s build something extraordinary together.

Dream's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Central to our Dream's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.

At Dream, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.

The Dream Job:

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI systems and ML pipelines to join our AI group. You'll be responsible for designing, building, deploying, and maintaining production-grade AI systems and ML pipelines. You’ll work closely with data scientists to translate research into scalable solutions and manage model deployment in both cloud and on-prem GPU environments.

The Dream-Maker Responsibilities:

• Design, build, and deploy production-grade ML models, AI agents, and end-to-end pipelines across cloud and on-prem GPU environments.
• Maintain and optimize ML systems for performance, scalability and reliability, including model validation, inference speed, and resource efficiency.
• Develop monitoring and observability tools such as alerts and performance metrics to ensure system stability in production.
• Create and integrate APIs for ML services within microservice-based architectures.
• Drive adoption of best practices for CI/CD, observability, and reproducibility in ML systems.
  
  

• 3+ years of experience delivering production-grade ML/AI systems
• Strong Python skills and solid understanding of the ML lifecycle
• Experience with GPU infrastructure, containerization (Docker) and cloud platforms
• Familiarity with microservice architectures and API development
• Hands-on experience with LLM pipelines and agent orchestration frameworks (LangGraph, LlamaIndex, etc.)
• Knowledge of experiment tracking tools (Weights & Biases, MLflow, or similar)
• Background in scalable ML infrastructure, distributed computing, and workflow orchestration frameworks (Ray, Kubeflow, Airflow)
• Experience with multi-node training (advantage)
• Collaborative mindset with startup-level ownership and pragmatism