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About Accenture:

Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world. We bring together the talent of our approximately 814,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes at scale. Through our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. We serve approximately 9,000 clients and generated approximately $74 billion in FY26 revenue. Visit us at accenture.com.

We are:

A forward-thinking services company at the forefront of AI-native engineering. We partner with enterprise clients to design, build, and run production systems in which AI is a first-class engineering capability, engineered into the product and into the way our teams build it. Our engineers embed deeply with customers, own the code they ship, and move work past experimentation into operational reality.

You are:

An engineer who builds across the stack: front end, APIs, services, data, and the infrastructure it runs on. You take ownership of a feature through to production, including the parts that sit outside your strongest layer, and you have shipped software that real users depend on. You can explain the design decisions behind your work and what you would do differently.

AI is part of how you work. You use AI tools daily to generate, test, debug, and document code, and you have built at least one production feature in which a model performs meaningful work. You understand what it takes for a system to hold up under production load, security review, and real users.

You'll grow into an engineer who can own an entire system, developing depth in cloud, security, data, and agentic engineering alongside senior engineers who remain hands-on in the code with you.

The Work:

You'll work as part of a client-embedded engineering team, taking real features from design through production. You will be handed problems to solve, and you will be expected to ask precise questions, make progress under ambiguity, and put working software in front of stakeholders quickly.

Build Across the Stack

+ Build and ship production features across front end, APIs, backend services, integration, and data, taking a feature end to end across every layer it touches.

+ Read and debug unfamiliar code, trace a problem across application, data, and infrastructure boundaries, and fix the underlying cause.

+ Build and Run on Cloud-Native Platforms

+ Develop, containerize, and deploy services on a major cloud platform using Docker, Kubernetes or serverless, and managed cloud services.

+ Apply infrastructure as code (Terraform, Helm, or equivalent), and take part in keeping your services healthy, observable, and recoverable in production.

+ Work Inside the Delivery Pipeline

+ Work within automated CI/CD pipelines, writing the automated tests, resolving pipeline failures, and taking your own changes through to production.

+ Apply secure engineering practices as you code, including identity and access fundamentals, secrets handling, dependency hygiene, and input validation.

Work With Data and Context:

+ Design and implement the data access your features need across relational and non-relational stores, APIs, object storage, and event platforms.

+ Build the retrieval and context layers AI features depend on, including knowledge sources, embeddings, metadata, and context management, applying data quality, access, and privacy controls as you go.

Build AI Features and Agent Workflows:

+ Integrate commercial and open models through APIs and enterprise AI platforms, using tool calling, structured outputs, retrieval, and context engineering to make them useful inside live business workflows.

+ Build agent workflows with tools, memory, and human checkpoints, and write the evaluations that measure their behavior against accuracy, latency, safety, and cost.

Engineer With AI Every Day:

+ Use AI-native development techniques throughout your own workflow, including code generation, test generation, debugging, refactoring, documentation, and code comprehension, and measure the effect on your throughput and quality.

+ Give agents the context, tools, and boundaries they need to safely handle bounded engineering tasks, and review their output to the same standard you would apply to a colleague's code.

Grow and Contribute:

+ Participate in design reviews, code reviews, and production troubleshooting, applying feedback quickly and raising the quality of the work around you.

+ Document what you learn and contribute patterns, reusable components, and tooling back to the team and to our internal AI-native engineering community.

Travel may be required for this role. The amount of travel will vary from 25% to 75% depending on business need and client requirements.

Here's What You Need:

+ Minimum of 3 years of professional software engineering experience building production software.

+ Minimum of 2 years of hands-on experience working across more than one layer of the stack, for example front end and API, or services and data.

+ Minimum of 3 years of programming experience in at least one major language such as Java, Python, Go, TypeScript/JavaScript, or C#.

+ Minimum of 3 year of hands-on experience with a major cloud platform (AWS, Azure, or GCP), including building and deploying containerized services.

+ Minimum of 3 year of experience working within an automated CI/CD pipeline, including writing automated tests.

+ Minimum of 1 year of hands-on experience building and shipping AI-enabled features or agent workflows, including model integration through APIs, tool calling, structured outputs, retrieval, and context engineering.

+ Minimum of 1 year of experience applying AI coding tools to day-to-day software development, including code generation, test generation, debugging, and documentation

+ Bachelor's degree in Computer Science, Engineering, or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience)

Bonus Points If You Have:

+ A public repository, portfolio, or open-source contribution featuring agents, tools, or plugins you built yourself.

+ Experience with an agent framework such as LangGraph, Crew AI, the Claude Agent SDK, or the OpenAI SDK.

+ Experience writing evaluations or test harnesses that measure AI behavior.

+ Hands-on experience with infrastructure as code and observability tooling.

+ Relevant cloud or AI certifications.

+ Experience in a client-facing or product-facing role presenting your own technical work to stakeholders.

+ Experience in a startup, product company, or...

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