Responsible AI Engineering Senior Manager

Accenture; Atlanta (GA); On-site; USD 140,000 - 200,000; Full time

Job summary

Accenture is seeking a Responsible AI Senior Manager to lead the design, delivery, and governance of production-grade AI and agentic systems at scale. You will guide onshore and offshore teams, embedding accountability and safety into architecture, models, and workflows across cloud ecosystems.

The role emphasizes building trusted AI through technical fluency, governance committees, and rigorous risk assessments, driving responsible AI adoption across client engagements.

Responsibilities

• Embed Responsible AI across the lifecycle with fairness, explainability, governance, privacy, and safety controls.
• Own AI risk and governance as the senior technical authority on responsible use.
• Lead large onshore and offshore AI teams delivering production-grade solutions.
• Architect enterprise AI platforms with guardrails across multiple providers and clouds.
• Set reference architectures and standards for LLMOps and data science workflows.

Job description

We Are:

Accenture is a leading global professional services company that helps the world's leading businesses, governments, and other organizations build their digital core, optimize their operations, accelerate revenue growth, and enhance citizen services, creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 790,000 people serving clients in more than 120 countries, and we believe that scaling AI responsibly is what makes it durable. Technology is at the core of change today, and we combine our strength in technology and leadership in cloud, data, and AI with unmatched industry experience, functional expertise, and global delivery capability.

Our Responsible AI capability, within our broader AI & Data practice, sits at the intersection of deep industry knowledge, applied AI engineering, and trusted AI governance. We help the world's leading organizations build and run production-grade AI and agentic systems that are safe, fair, transparent, and compliant by design, not by afterthought. We are not looking for generalists who advise on AI risk in the abstract. We need a practitioner who has built, shipped, and run AI systems at scale, who understands exactly where the technical, ethical, and operational risks live, and who can engineer the guardrails directly into the architecture and the delivery model, across both onshore and offshore teams.

You Are:

You are a Responsible AI Senior Manager who makes trustworthy AI real by building it, not just reviewing it. You bring that credibility because you are, at your core, a deeply technical AI and Data delivery leader: you have designed and shipped production AI, agentic AI, and data science solutions yourself, and you have led large, distributed delivery teams across onshore and offshore locations to do the same at scale. You are equally comfortable in a model architecture review, an agentic workflow design session, and a governance committee, and you use that technical fluency to embed fairness, transparency, safety, and compliance directly into how systems are designed, built, tested, and operated. You set the technical and ethical standard for how generative and agentic AI gets built, secured, and run, and you lead the engineering, data science, and delivery teams who build it.

The Work:

• Embed Responsible AI across the lifecycle — define and operationalize Responsible AI standards, including fairness and bias testing, explainability, model and data governance, privacy, safety evaluations, and human oversight, and build these controls directly into engineering and MLOps/LLMOps pipelines rather than bolting them on after the fact.

• Own AI risk and governance — serve as the senior technical authority on responsible use of generative and agentic AI, running model risk assessments, red-teaming and safety evaluations, and advising clients and internal leadership on regulatory, ethical, and reputational AI risk.

• Lead large-scale, global delivery teams responsibly — manage and mentor large onshore and offshore AI, data science, and engineering teams delivering production-grade AI and agentic AI solutions, owning staffing, technical direction, quality, and delivery outcomes across geographies and time zones, with Responsible AI checkpoints built into every stage of delivery.

• Architect and build production AI and agentic systems with guardrails in place — personally guide the architecture, design, and delivery of enterprise AI, agentic AI, and data science platforms spanning multiple model providers (OpenAI, Anthropic, and others) and cloud ecosystems (AWS, Azure, Google), ensuring solutions are production-grade, scalable, maintainable, and governed.

• Set technical standards and reference architectures grounded in Responsible AI — define reference architectures and technical standards for LLMOps, agentic AI orchestration, data science workflows, security, reliability, and cost governance across the