Responsible AI Engineering Senior Manager

What you'll do

• Define and operationalize Responsible AI standards including fairness, bias testing, and explainability within MLOps pipelines.
• Serve as the senior technical authority for generative and agentic AI risk, performing red-teaming and safety evaluations.
• Manage and mentor large, distributed onshore and offshore engineering and data science teams to deliver production-grade AI.
• Architect and build production-grade AI and agentic systems across multiple cloud ecosystems and model providers.
• Establish reference architectures and technical standards for LLMOps, security, reliability, and cost governance.
• Implement FinOps-for-AI practices to optimize costs and embed security controls into platform designs.
• Partner with clients and pursuit teams to shape solution architectures and contribute to sales and proposal efforts.
• Develop playbooks, training, and governance frameworks to increase the technical maturity of the practice.

What we're looking for

• Minimum of 10 years of experience in software, AI/ML engineering, or data science, including architecture and technical leadership.
• Minimum of 6 years of experience architecting and delivering enterprise AI, agentic AI, or data science platforms in production.
• Minimum of 5 years of experience leading large, distributed teams, including managing both onshore and offshore delivery teams.
• Minimum of 5 years of experience delivering production-grade AI use cases end to end with built-in governance and risk controls.
• Minimum of 3 years of experience with Responsible AI, AI governance, model risk management, or AI ethics.
• Minimum of 3 years of experience with LLMOps/MLOps at scale and multi-cloud architecture (AWS, Azure, Google).
• Minimum of 1 year of hands-on experience designing and building agentic AI architectures.
• Bachelor's degree or equivalent (minimum 12 years' work experience); Associate's degree requires minimum 6 years' work experience.