Responsible AI Engineering Senior Manager

Accenture•St. Louis, MO

1d•$112,900 - $338,300•Hybrid

About The Position

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 that believes 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 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.

Requirements

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, across major cloud and model providers
Minimum of 5 years of experience leading large, distributed teams, including direct experience managing both onshore and offshore delivery teams
Minimum of 5 years of experience delivering production-grade AI use cases end to end, from prototype through deployment and operations, with governance and risk controls built in
Minimum of 3 years of experience with Responsible AI, AI governance, model risk management, or AI ethics, including hands-on work on fairness, explainability, privacy, or safety evaluation
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, including multi-agent orchestration, tool use, and autonomous workflow design
Bachelor's degree or equivalent (minimum 12 years' work experience). If Associate's Degree, must have equivalent minimum 6-year work experience

Nice To Haves

You hold a certification or formal training in Responsible AI, AI governance, or AI risk management
You have demonstrated thought leadership or published work in Responsible AI, AI engineering, or agentic AI
You have experience standing up AI governance functions or Responsible AI programs from the ground up
You have a Master's degree or PhD in a relevant field such as Computer Science, Data Science, or AI/ML
You hold cloud architecture or AI engineering certifications (AWS, Azure, or Google)
You have external client-facing consulting experience

Responsibilities

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 AI estate, and hold engineering teams accountable to them.
Drive cost and security governance — establish FinOps-for-AI cost optimization practices and embed AI security and governance controls into platform design and delivery.
Shape responsible AI solutions and grow the business — partner with clients and pursuit teams to shape solutions, architectures, and delivery models for major AI and Responsible AI programs, and contribute to Accenture sales and proposal efforts when needed.
Build organizational capability in Responsible AI — develop playbooks, training, and governance frameworks that raise the Responsible AI and technical delivery maturity of teams across the practice, and continue to deepen your own expertise in AI engineering, agentic AI, data science, and Responsible AI.