Responsible AI Engineering Senior Manager

Written by the T&S Network from Accenture's posting.

Responsible AI engineering leadership for production generative and agentic AI systems, embedding fairness, explainability, privacy, safety evaluation and human oversight into architecture, MLOps/LLMOps pipelines and delivery. The role sits in Accenture's Responsible AI capability within its AI & Data practice and leads large onshore and offshore engineering and data science teams building client AI solutions.

What you'd do

• Write Responsible AI standards and turn them into working practice across fairness, explainability, data governance and human review.
• Put responsible AI checks inside engineering and MLOps/LLMOps pipelines from the start, not added at the end.
• Test generative and agentic systems through risk assessments, adversarial red-teaming and safety evals.
• Counsel clients and company leaders on regulatory, ethical and reputation risk from AI.
• Lead and coach big AI, data science and engineering groups split across onshore and offshore sites.
• Define the reference designs and technical rules for LLMOps, agent orchestration, security and cost control.
• Guide architecture and delivery of AI platforms across multiple model providers and cloud environments.

What it asks for

• Ten or more years in software, AI/ML engineering or data science, with architecture and tech leadership.
• Five or more years leading large distributed teams, including onshore and offshore delivery.
• Five or more years delivering production AI use cases end to end with governance and risk controls.
• Three or more years of Responsible AI, AI governance, model risk or AI ethics work, including fairness, explainability, privacy or safety evaluation.
• Three or more years running LLMOps/MLOps at scale across AWS, Azure and Google.
• At least one year hands-on building agentic architectures with multi-agent coordination and tool use.

Worth knowing

• Travel may be required, from 0% to 100% depending on business need.

Terms in this role

Red teaming.

Testing a system, often an AI model, by deliberately trying to make it fail or misbehave, so problems are found and fixed first.

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