Machine Learning Engineer - Applied AI

3+ years proven track record building and deploying AI/ML solutions in live environments Expert-level Python programming abilities Shipped and maintained AI/ML systems, not just prototypes Demonstrated interest in cybersecurity and penetration testing Knowledge of AI frameworks and machine learning libraries (such as TensorFlow, PyTorch, Hugging Face, OpenAI APIs, and LangChain) Experience with ML data preprocessing techniques including normalization, feature engineering, and dataset preparation for model training and fine tuning Experience with cloud AI platforms such as AWS Bedrock, AWS SageMaker, and Azure AI, with ability to implement and optimize solutions across environments Strong analytical skills and ability to clearly communicate complex security concepts Comprehensive understanding of various database technologies, including relational and non-relational systems

Build sophisticated AI systems capable of discovering vulnerabilities and streamlining penetration testing methodologies Research and implement AI-driven vulnerability detection and exploitation methods Automate exploitation workflows using AI technologies Lead complete development lifecycles for AI features from concept through deployment Design and implement advanced testing capabilities that detect vulnerabilities in AI-native applications Research and target emerging attack vectors specific to applications developed with AI and implement detection methods Implement scalable solutions that can use AI to exploit traditional CVEs, credential-based attack paths, and data pilfering vectors Guide technical conversations, assess code quality, and facilitate design meetings while sharing knowledge to enhance team capabilities Support and guide less experienced team members, fostering their professional development