AI Native Software Engineer – Products

Accenture•Seattle, WA

1d•Hybrid

About The Position

Accenture is building a new portfolio of agentic software products in the industries they have spent decades helping to transform. They have advantages such as deep industry expertise, trusted relationships with large organizations, and direct access to complex workflows and problems. This allows them to build closely with customers while creating repeatable products that can be taken broadly to market. Small, highly capable product teams are being formed to do this, bringing 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, not a consulting delivery, data engineering, product management, or forward-deployed engineering role.

Requirements

Minimum of 3 to 8 years of experience building and shipping production software.
Minimum of 3 years experience building across the full stack, including frontend and backend systems.
Minimum of 3 years of software engineering fundamentals, including system design, data modeling, APIs, testing, cloud-native development, security, observability, and production operations.
Minimum 1 year of substantive, hands-on AI-native engineering experience as part of your day-to-day work. This may include building with LLM APIs, coding agents, agent frameworks, tool integrations, retrieval systems, evaluation frameworks, or production guardrails.
Minimum 1 year of experience building reliable AI or agentic systems that operate beyond a prototype or demonstration environment including a track record of taking products, systems, or major capabilities from zero to one and owning them through production.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience.)
Demonstrated ability to use AI development tools to materially increase engineering leverage without lowering the quality bar.
Excellent judgment under ambiguity and the ability to make progress without complete information.
Strong product instincts and the ability to recognize what makes a product useful, intuitive, and well crafted.
The ability to communicate technical decisions clearly and work closely with product managers, designers, customers, and domain experts.
A genuine interest in frontier AI and a consistent habit of testing new models, tools, and engineering approaches in practice.

Responsibilities

Build and ship across the full stack, owning 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.
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.
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.
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.
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.
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.