AI Engineer, BlackCloak

• Proven Experience: Demonstrated ability to ship production-grade AI/ML models (e.g., LLMs, Neural Networks) that have served a large user base. A link to your GitHub or portfolio with examples of your work is preferred
• Full-Stack ML Proficiency: Strong experience across the entire model training and development lifecycle: from requirements gathering and data analysis to deployment, monitoring, and retraining
• Strong Collaboration Skills: A track record of working effectively with cross-functional teams, including product managers, software engineers, and DevOps. Exceptional skills working with a team to ensure best practices for model development, deployment, documentation and more are met
• Extreme Ownership: An ownership mindset with a deep sense of responsibility for the success of projects and the team. You're a clear, proactive communicator who takes initiative and sees things through to completion

Desirable

• In this role, you will be leading the development and release of AI agents and ML models into our core product and business operations. You'll have the opportunity to work with product leaders, engineers, and leaders in security to see ideas from inception to experiments and all the way through to end user facing products and services
• End-to-End Model Development: Own the entire lifecycle of AI/ML models, from initial research and experimentation to production deployment. You'll design, develop, and train models from the ground up, including neural networks, GBMs, and large language models (LLMs)
• Build and Scale Production Pipelines: Take models from the lab to a live, deployable service. Collaborate with DevOps and Software Engineering to build robust MLOps pipelines that take trained models and LLM agents into our production environment. Ensure model evals and observability enable scalable retraining and iteration to maximize performance over time
• Validate and Communicate: Apply a data-driven approach to design and execute rigorous experiments. You'll track key performance metrics and communicate results to both technical and non-technical stakeholders to help inform go/no-go decisions on new initiatives
• Research & Ideation: Conduct research and evaluate new machine learning and AI techniques to solve complex problems related to digital security, threat detection, and data analysis