Rovo & AI Prompt Engineer

Accenture•Boston, MA

2d•Hybrid

About The Position

Strategic Technology Effectiveness, part of Accenture's Technology Strategy & Transformation Organization, rebuilds how large enterprises define, design, and govern software, and designs and implements the agentic software and processes that run it. This 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. The team starts with evidence, using metrics like cycle time, flow efficiency, WIP, and throughput to identify delivery bottlenecks. The solutions designed are AI-native by construction, redesigning intake and prioritization so demand meets real capacity, and rebuilding the path from request to release to connect planning, build, review, testing, and deployment. Much of the build work focuses upstream, where the cost of rework is set: requirements generation, user story synthesis, design review, and architecture validation. This involves creator and reviewer agent patterns, tool-use orchestration, and multi-agent workflows on enterprise frameworks, with an understanding of token costs and potential breaking points. The lifecycle is treated as a system and a product itself, built to operate at the speed required for the AI era, and engineered to stick. The goal is for engineering teams to move to AI-augmented work and stay there, distinguishing a delivery organization where AI is how software gets made from a working pilot. Practitioner depth in delivery operating models and AI, paired with Accenture's global platform and technology expertise, positions the team to work with delivery organizations at a scale few firms can match.

Requirements

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)

Nice To Haves

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

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.