Rovo & AI Prompt Engineer

Accenture•Miami, FL

4d•$80,400 - $266,300•Hybrid

About The Position

Accenture's Strategic Technology Effectiveness team, part of the Technology Strategy & Transformation Organization, focuses on redefining how large enterprises manage software development. They design and implement AI-native agentic software and processes, transforming operating models, ways of working, and the supporting tooling. The team utilizes data-driven insights (cycle time, flow efficiency, WIP, throughput) to identify delivery bottlenecks and design AI-native solutions. Their work involves redesigning intake and prioritization processes to align demand with capacity and rebuilding the request-to-release path for seamless integration of planning, build, review, testing, and deployment. A significant portion of their build work occurs upstream in areas like requirements generation, user story synthesis, design review, and architecture validation, focusing on creator and reviewer agent patterns, tool-use orchestration, and multi-agent workflows. They treat the entire lifecycle as a product, optimized for the speed of the AI era. The goal is to ensure engineering teams adopt and sustain AI-augmented work, making AI integral to software development. This is achieved through deep practitioner expertise in delivery operating models and AI, combined with Accenture's global platform and technology capabilities.

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, optimize, and govern enterprise AI experiences using Atlassian Rovo and LLMs.
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.