AI Native Software Engineering Consultant

About

ARE YOU READY to step up and take your technology expertise to the next level?

There is never a typical day at Accenture, but that’s why we love it here! This is an extraordinary chance to begin a rewarding career at Accenture Technology. Immersed in a digitally compassionate and innovation-led environment, here is where you can help top clients shift to the New using leading-edge technologies on the most ground-breaking projects imaginable.

Cloud First - Software Engineering is a high performing team to join! We focus on client adoption of disruptive technologies, technology architecture and providing specialized skills related to integration, custom software engineering, testing, application modernization, agile and more. We help our clients with the most complex projects including working in open web platforms, DevOps platforms as well as intelligent Computing and Architecture enhancement.

The Work

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities

• Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.
• Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.
• Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance, healthcare, retail — addressing domain-specific processes via intelligent automation.
• Client Engagement: Conduct design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
• Measure & Improve: Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
• Knowledge Sharing: Craft reusable patterns, documentation, and best practices to influence internal assets and client roadmaps.