Develop and implement enterprise-scale Responsible AI systems and governance frameworks, ensuring they meet the ethical, performance, and security requirements for mission-critical applications.
Contribute to the architecture and implementation of a centralized "Responsible AI Framework" to ensure compliance, manage model access, and provide a unified interface for governance and risk management.
Implement and manage robust monitoring systems to track model performance, fairness, bias, and ethical compliance, and to optimize the cost-effectiveness of AI systems in production.
Work closely with principal engineers, data scientists, and systems architects to translate strategic designs into hardened, production-grade solutions that embed fairness, accountability, and transparency principles.
Establish and maintain robust AI governance frameworks and guardrails to ensure data integrity, filter inputs/outputs, prevent bias, mitigate deployment risks, and protect against adversarial attacks.
Apply and promote software engineering best practices, including robust version control, comprehensive automated testing, and mature CI/CD processes for AI systems.
Stay current with industry trends in Responsible AI, Explainability (XAI), operational AI, and MLOps to continuously evolve the team's capabilities and technical implementation.

Requirements

A Bachelor's degree in Computer Science, Engineering, or a related quantitative field with 4+ years of professional experience, OR a Master's degree with 2+ years of relevant experience.
Demonstrated programming proficiency in Python and hands-on experience with major ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience with software engineering best practices and tools, including version control, automated testing, and CI/CD pipelines.
Solid understanding of the full machine learning lifecycle, from data preparation and model training to deployment and monitoring.
A strong understanding of Responsible AI principles, ethical AI practices, and techniques for bias detection and mitigation.
An understanding of cybersecurity principles as they apply to AI systems, including threat modeling and vulnerability assessment.
Must be a U.S. Citizen and have the ability to obtain and maintain a U.S. security clearance.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonTensorFlowPyTorchScikit-learnmachine learning lifecycleautomated testingCI/CDversion controlethical AI practicesbias detection

collaborationcommunicationproblem-solvingadaptabilityleadershipcritical thinkingattention to detailstrategic thinkingaccountabilitytransparency