AI Native Software Engineer Consultant – Products

Accenture•Seattle, WA

19h•Hybrid

About The Position

Accenture is forming small, autonomous product teams to build and take a new portfolio of agentic software products from prototype through production and scale. This is a software engineering role within a permanent product team, not a consulting delivery, data engineering, product management, or forward-deployed engineering role. The engineer will build and ship production products across the full stack, combining engineering fundamentals, product judgment, and systems thinking. AI is central to both the products and the daily engineering workflow: the role uses coding agents and other AI development tools while maintaining accountability for architecture, security, reliability, and quality. This hybrid role requires onsite work in Accenture offices in Seattle, New York, and/or Mountain View, and may include travel ranging from 0% to 100%.

Requirements

At least 3 years of experience building and shipping production software; the posting contains the wording “Minimum of 3 years eight years of experience.”
At least 3 years of experience building across the full stack, including frontend and backend systems.
At least 3 years of software engineering fundamentals, including system design, data modeling, APIs, testing, cloud-native development, security, observability, and production operations.
At least 1 year of substantive, hands-on AI-native engineering experience in day-to-day work, potentially including LLM APIs, coding agents, agent frameworks, tool integrations, retrieval systems, evaluation frameworks, or production guardrails.
At least 1 year of experience building reliable AI or agentic systems beyond prototype or demonstration environments, including taking products, systems, or major capabilities from zero to one and owning them through production.
Bachelor’s degree or equivalent, with a minimum of 12 years of work experience; if holding an associate’s degree, a minimum of 6 years of work experience is required.
Demonstrated ability to use AI development tools to materially increase engineering leverage without lowering the quality bar.
Excellent judgment under ambiguity and ability to make progress without complete information.
Strong product instincts and ability to recognize what makes a product useful, intuitive, and well crafted.
Ability to communicate technical decisions clearly and work closely with product managers, designers, customers, and domain experts.
Genuine interest in frontier AI and a consistent habit of testing new models, tools, and engineering approaches in practice.
For U.S. applicants, work authorization that does not now or in the future require visa sponsorship.

Responsibilities

Own product capabilities end to end, spanning application architecture, data models, backend services, APIs, integrations, AI systems, and user experiences.
Collaborate with product managers and designers to translate customer problems into simple, high-quality products.
Move between prototyping and production engineering, balancing speed with scale, security, and reliability.
Design and implement agentic systems, including model interactions, tool use, retrieval, orchestration, state management, evaluations, observability, and guardrails.
Build AI products that work reliably in complex, real-world industry workflows; assess system behavior and failure modes and improve products using evidence.
Use coding agents and AI development tools across the engineering lifecycle; create the context, specifications, tests, development environments, CI pipelines, and evaluation systems that enable safe, efficient agent-assisted work.
Direct multiple streams of agent-assisted work where appropriate, critically review their output, and remain accountable for production deliverables.
Translate ambiguous product opportunities into technical approaches and executable plans; make and carry architecture decisions from prototype through production.
Balance speed, simplicity, extensibility, security, cost, and operational reliability, and determine what to build, buy, integrate, or defer.
Stay close to users and customers, understand their workflows, and use feedback, product data, evaluations, and operational signals to continuously improve the product.
Own production reliability, observability, security, performance, and maintainability; treat adoption and customer outcomes as engineering concerns.
Provide rigorous technical review, help engineers improve their judgment and AI-native development practices, and contribute to shared architecture, tools, standards, and operating practices.
Help define how small, AI-native product teams should operate as the product engineering organization grows.