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Accenture is building a new portfolio of agentic software products and is forming compact product teams that include product management, design, and engineering with autonomy to move quickly. This software engineering role focuses on AI-native systems—from prototypes to production—within a permanent product team, not consulting delivery.
You will own end-to-end product capabilities, design agentic systems, and contribute to measurable customer outcomes while maintaining security, reliability,
Accenture is building a new portfolio of agentic software products in the industries we have spent decades helping to transform. We begin with advantages most product companies do not have: deep industry expertise, trusted relationships with many of the world’s largest organizations, and direct access to the complex workflows and problems that matter most to them. That allows us to build closely with customers while creating repeatable products that can be taken broadly to market.
We are forming small, highly capable product teams to do this. These teams bring product management, design, and engineering together with the autonomy to move quickly, make decisions, and own outcomes from the earliest prototype through production and scale.
This is a software engineering role within a permanent product team. It is not a consulting delivery, data engineering, product management, or forward-deployed engineering role.
You are an exceptional AI-native software engineer who has built and shipped production products across the full stack. You combine strong engineering fundamentals with product judgment, systems thinking, and a high bar for quality. You are comfortable beginning with an ambiguous customer or business problem, shaping the technical approach, and turning it into software that real users rely on.
AI is central to both what you build and how you build. You use coding agents and other AI development tools as a core part of your daily workflow. You know how to decompose work, provide the right context and constraints, establish tests and evaluations, and review generated output with rigor. You use AI to increase your leverage while retaining full accountability for the architecture, security, reliability, and quality of the product. You are energized by zero-to-one work, small teams, high autonomy, and the opportunity to help define how a new product engineering organization should operate.
Build and ship across the full stack. Own meaningful product capabilities end to end, including application architecture, data models, backend services, APIs, integrations, AI systems, and user experiences. Work directly with product managers and designers to turn customer problems into simple, high-quality products. Move comfortably between prototyping and production engineering, making thoughtful decisions about when to optimize for speed and when to invest for scale, security, and reliability.
Build AI-native products. Design and implement agentic systems, including model interactions, tool use, retrieval, orchestration, state management, evaluations, observability, and guardrails. Build products that perform reliably against complex, real-world industry workflows rather than only in controlled demonstrations. Evaluate model and system behavior rigorously, understand failure modes, and improve the product through evidence.
Work in an AI-native engineering model. Use coding agents and AI development tools extensively throughout the engineering lifecycle. Create the context, specifications, tests, development environments, CI pipelines, and evaluation systems that allow agents to produce high-quality work safely and efficiently. Direct multiple streams of agent-assisted work where appropriate, review outputs critically, and remain accountable for everything that reaches production.
Set technical direction. Translate ambiguous product opportunities into clear technical approaches and executable plans. Make sound architecture decisions for new product areas and carry those decisions from prototype through production. Balance speed, simplicity, extensibility, security, cost, and operational reliability. Identify where the team should build, buy, integrate, or deliberately defer.
Own product and production outcomes. Stay close to users and customers and understand the workflows your software is changing. Use customer feedback, product data, evaluations, and operational signals to improve the product continuously. Own reliability, observability, security, performance, and maintainability in production. Treat successful adoption and customer outcomes as engineering concerns.
Raise the engineering bar. Provide rigorous technical review on the work that matters most. Help other engineers improve their judgment, execution, and use of AI-native development practices. Contribute to the shared architecture, tools, standards, and operating practices of the broader product engineering organization. Help define how small, AI-native product teams should work as the company grows.
U.S. Employee Benefits | Accenture (https://www.accenture.com/us-en/careers/local/benefits)
California $94,400 to $316,300 Colorado $94,400 to $273,200 Connecticut $94,400 to $273,200 District of Columbia $100,500 to $291,000 Illinois $87,400 to $273,200 Maine $80,400 to $232,800 Maryland $94,400 to $273,200 Massachusetts $94,400 to $291,000 Minnesota $94,400 to $273,200 New York $87,400 to $316,300 New Jersey $100,500 to $316,300 Ohio $87,400 to $253,000 Virginia $87,400 to $291,000 Washington $100,500 to $291,000
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