The Work

• Design, build, and configure individual agents - including their prompts, tools, and skills - and integrate them into multi-agent workflows
• Implement agent orchestration logic that handles task handoffs, communication, and error recovery
• Build evaluation harnesses and test suites that measure agent and component quality on metrics such as accuracy, relevance, and faithfulness, and share findings to inform design improvements
• Integrate foundation models into applications, selecting the appropriate model and invocation pattern for each use case
• Build and run model fine-tuning pipelines - including data preparation and training - to adapt models to specific business domains, applying working knowledge of transformer-based architectures
• Build ingestion pipelines that parse, chunk, enrich, and index unstructured enterprise content for retrieval
• Implement embedding generation, integrate vector databases, and develop retrieval components, including connectors and adapters that process unstructured content into end-to-end RAG pipelines
• Build the logic that assembles prompts and manages what information is passed to the model within its context window
• Implement memory components that store and recall conversational history and other relevant context
• Implement input/output guardrails, content filtering, and defenses against prompt injection
• Build PII detection and redaction components and integrate access controls for model and tool access
• Implement versioning, audit logging, and lineage tracking, and maintain model documentation that keeps the system auditable
• Instrument components with logging and tracing for requests, responses, token usage, and tool calls
• Contribute to monitoring, alerting, and cost tracking that keep AI systems healthy in production
• Continuously learn and apply new design patterns, technologies, and frameworks across the fast-evolving AI landscape, bringing fresh approaches to the components you build
• Collaborate within cross-functional teams to clarify requirements and ensure your components meet stakeholder needs
• Create and maintain clear technical documentation for the components you build, supporting troubleshooting and future development

Education

• Bachelor's Degree in Computer Science, Computer Engineering, Data Science, or a related engineering discipline

Basic (required) Qualification

• Experience (work or coursework) in designing, coding, building advanced AI solutions using agentic, generative, and classical AI/ML using at least one cloud vendor.
• Experience (work or coursework in the Agentic, LLM and Generative AI space.
• Experience (work or coursework) architecting and operationalizing LLM-driven application architecture patterns.
• Experience in coding and engineering, machine learning, deep learning, and NLP solutions and applications.
• Coding experience using Python

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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