We Are

Strategic Technology Effectiveness, part of Accenture's Technology Strategy & Transformation Organization. We rebuild how large enterprises define, design and govern software, and we design and implement the agentic software and processes that run it. That work changes the operating model an engineering organization runs on, the ways of working underneath it, and the tooling that makes both of those real.

We start with evidence. Cycle time, flow efficiency, WIP and throughput show where delivery stalls, and that diagnosis sets the target. What we design from it is AI-native by construction. We redesign intake and prioritization, so demand meets real capacity, and rebuild the path from request to release so planning, build, review, testing and deployment connect to each other instead of handing off through spreadsheets and status meetings.

Most of our build work sits upstream, where the cost of rework gets set: requirements generation, user story synthesis, design review, architecture validation. That means creator and reviewer agent patterns, tool-use orchestration, and multi-agent workflows on enterprise frameworks, with a working understanding of what each pattern costs in tokens and where it breaks. We treat the lifecycle as a system and a product itself - built to operate at a speed required for the AI era.

Then we make it stick. Engineering teams move to AI-augmented work and stay there, which is what separates a delivery organization where AI is how software gets made from the working pilot.  Practitioner depth in delivery operating models and AI, paired with Accenture's global platform and technology expertise, puts us in front of delivery organizations at a scale few firms reach.

The Work

Design, optimize, and govern enterprise AI experiences using Atlassian Rovo and LLMs. This role combines prompt engineering, knowledge orchestration, AI agent development, and workflow automation to deliver secure, accurate, and scalable AI capabilities that improve productivity and decision-making.

Role-Specific Responsibilities

Design enterprise AI prompts and conversational experiences.
Develop and maintain Atlassian Rovo Agents.
Build AI-powered knowledge assistants and workflow automations.
Curate enterprise knowledge to improve AI-generated responses.
Establish prompt libraries, standards, and governance processes.
Evaluate and optimize AI performance using quantitative and qualitative metrics.
Collaborate with product owners, solution architects, AI engineers, and business stakeholders.
Promote AI adoption and prompt engineering best practices across the organization

Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.

Here's What You Need

Minimum of 5 years of experience in a technical role involving AI/ML systems, NLP, conversational AI, or knowledge management — with at least 2 years working directly with LLMs
Minimum of 2 years of hands-on prompt engineering experience, including designing, testing, and iterating prompts for enterprise use cases across one or more LLM platforms (e.g., OpenAI, Anthropic, or similar)
Minimum of 2 years' experience helping enterprise clients design, adopt, or govern AI-powered tools, knowledge systems, or automation workflows
Minimum of 2 years' experience of demonstrating building or configuring AI agents, virtual assistants, or chatbot experiences — including tool-use, knowledge grounding, and persona design
Minimum of 2 years of experience with knowledge management systems (e.g., Confluence, SharePoint, or similar) and ability to curate and structure enterprise content to improve AI output quality
Minimum of 2 years of experience and proven ability to evaluate AI system performance, identify failure modes, and drive iterative improvement through structured testing and stakeholder feedback
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience

Bonus points if you have

Hands-on experience with Atlassian Rovo — including agent configuration, knowledge connector setup, action/tool integration, and conversation design
Familiarity with Atlassian Intelligence features across the product suite (Jira, Confluence, JSM) and how they interact with Rovo agents and enterprise knowledge
Experience designing Rovo agent personas, scoping knowledge access, and establishing behavioral guardrails appropriate for enterprise deployment
Ability to integrate Rovo agents with enterprise workflows via Atlassian automation, APIs, or third-party connectors
Advanced proficiency in prompt engineering techniques including chain-of-thought reasoning, few-shot and zero-shot prompting, role prompting, structured output formatting, and retrieval-augmented generation (RAG) patterns
Ability to design prompts that are robust to input variation, resistant to prompt injection, and aligned to enterprise governance requirements
Experience with systematic prompt evaluation — including A/B testing, rubric-based scoring, and automated regression testing for prompt quality
Familiarity with token economics, context window management, and latency/cost tradeoffs in production LLM deployments
Working knowledge of model differences across leading LLM providers and ability to select or recommend appropriate models for specific enterprise use cases
Experience auditing, structuring, and curating enterprise knowledge bases (e.g., Confluence) to improve AI retrieval accuracy and reduce hallucination risk
Ability to establish and maintain prompt governance frameworks — including versioning, change control, approval workflows, and usage monitoring
Familiarity with AI safety, responsible AI principles, and enterprise compliance requirements as they apply to LLM-powered applications
Experience defining and tracking AI performance KPIs including response quality scores, deflection rates, task completion rates, and user satisfaction
Ability to facilitate AI discovery workshops with business stakeholders to surface use cases, prioritize opportunities, and define measurable success criteria
Experience developing prompt engineering playbooks, usage guidelines, and training materials to scale AI literacy across non-technical teams

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.

We anticipate this job posting will be posted until 11/30/2026.

Benefits

Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:

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Role Location Annual Salary Range

California $94,400 to $266,300

Cleveland $87,400 to $213,000

Colorado $94,400 to $230,000

District of Columbia $100,500 to $245,000

Illinois $87,400 to $230,000

Maine $80,400 to $196,000

Maryland $94,400 to $230,000

Massachusetts $94,400 to $245,000

Minnesota $94,400 to $230,000

New York $87,400 to $266,300

New Jersey $100,500 to $266,300

Virginia $87,400 to $245,000

Washington $100,500 to $245,000

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Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.

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