At Accenture Industry X we are building an AI Hub to design and scale agentic AI solutions—multi‑agent systems that can reason, plan, and take action by combining LLMs,retrieval‑augmented generation (RAG), and external tools/APIs.

In this role, you will develop the core building blocks for reliable agent systems: orchestration patterns, agent‑to‑agent collaboration, state and memory management, and production‑minded testing/observability. The goal is to move from prototypes to repeatable, robust, and maintainable agentic workflows.

Responsibilities

• Design and implement agent‑to‑agent communication patterns and multi‑agent collaboration workflows.
• Integrate LLMs,RAG pipelines, and external APIs/tools into end‑to‑end agent systems.
• Develop, test, and iterate on orchestration logic (routing, planning, tool selection, error handling, and structured execution).
• Build prompt templates,memory/state management, and reusable workflow components to improve reliability and consistency.
• Implement and optimize RAG + embedding workflows (retrieval strategy, chunking, evaluation, iteration).
• Own testing & debugging for multi‑agent systems, including tracing, evaluation, and regression testing across workflows.

Required skills

Please submit a CV that highlights your proven experience, with detailed descriptions of relevant home or personal projects

• Hands-on experience building LLM-based applications in Python, including structured prompting and multi-step workflows.
• Practical experience with at least one agent framework or orchestration approach(e.g.,Lang Chain or a comparable framework). Experience with **Lang Graph is a plus. **
• Working knowledge of prompt templates and basic memory/state concepts (e.g., session memory, conversation state, lightweight persistence).
• RAG fundamentals with some implementation exposure: ability to build a basic retrieval flow (data → chunking → embeddings → retrieval → grounded answer) and iterate on quality.
• Debugging mindset for agentic workflows: comfortable troubleshooting tool-calls, retrieval misses, and prompt/chain issues; familiar with logs/traces or basic observability practices.
• Strong English language knowledge.

Nice to have

• Lang Graph experience (graph/state-machine orchestration) and/or multi-agent patterns.
• Langfuse (or similar tracing/evaluation tooling) for prompt/version tracking, traces, and quality monitoring.
• Experience improving RAG quality beyond the basics (evaluation, retrieval strategies, chunking experiments).
• Exposure to testing practices for LLM apps (lightweight evaluation, regression checks, reproducibility habits).

About Accenture

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 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.
Visit us at www.accenture.com

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