AI Native Software Engineer
About the role
We Are A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.
Responsibilities
Partner directly with client stakeholders — acting as both technologist and trusted advisor.
Define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains.
Design and build enterprise-ready AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.
Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi-provider enablement.
Contribute to shared libraries, SDKs, and patterns that can be reused across clients.
Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability stacks to deliver scalable AI-native systems.
Own deployment, monitoring, and troubleshooting for your services in production.
Tailor and deploy agentic applications across verticals (e.g., finance, healthcare, retail), adapting to domain-specific processes and constraints.
Work closely with client SMEs to translate business workflows into agentic solutions.
Participate in and/or lead design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
Communicate trade-offs, risks, and recommendations clearly to both technical and non-technical audiences.
Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
Iterate rapidly based on data, feedback, and changing requirements.
Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
Contribute to internal communities of practice around AI-native and agentic engineering.
Requirements
Minimum of 3 years of engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 year of hands-on experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production or near-production environments.
Minimum of 1 year of experience with modern AI platforms — OpenAI, Claude, Vertex AI, or open-source models — including building or using abstraction layers for multi-provider pipelines.
Minimum of 3 years strong Python, Java or equivalent experience building 12 factor applications + Infrastructure as Code (Terraform, Helm).
Minimum of 3 years of experience in client-facing communication and collaboration, including leading technical discussions, workshops, or delivery sessions under ambiguity.
Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience.
If Associate’s Degree, must have minimum 6 years work experience.
Qualifications
Relevant AI certifications or agentic tooling experience are a plus.
Served as an Agentic / AI Engineer in an enterprise environment.
Built multi agent orchestrations using (Lang-graph, Crew AI, Claude SDK, Open AI SDK, etc).
Defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry / stream-based architectures.
Understood the AI-native paradigm — blending cloud-native with generative model architectures — optimizing for performance, modularity, and efficiency.
Delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs.
Driven execution across multiple workstreams, ensuring quality, delivery, and alignment with client outcomes.
Skills
Strong foundation in building cloud-native solutions.
Hands-on experience designing and deploying agentic systems, especially for enterprise environments.
Experience with modern AI platforms.
Experience with containerization, microservices, serverless, and observability stacks.
Ability to communicate effectively with both technical and non-technical stakeholders.
Experience with defining and using key metrics, test harnesses, and evaluation plans.
Experience with crafting reusable patterns, documentation, and best practices.
Benefits
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below. We anticipate this job posting will be posted until 06/23/2026.
Here’s What You Need
Minimum of 3 years of engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 year of hands-on experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production or near-production environments.
Minimum of 1 year of experience with modern AI platforms — OpenAI, Claude, Vertex AI, or open-source models — including building or using abstraction layers for multi-provider pipelines.
Minimum of 3 years strong Python, Java or equivalent experience building 12 factor applications + Infrastructure as Code (Terraform, Helm).
Minimum of 3 years of experience in client-facing communication and collaboration, including leading technical discussions, workshops, or delivery sessions under ambiguity.
Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Here’s What You Need
Relevant AI certifications or agentic tooling experience are a plus.
Served as an Agentic / AI Engineer in an enterprise environment.
Built multi agent orchestrations using (Lang-graph, Crew AI, Claude SDK, Open AI SDK, etc).
Defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry / stream-based architectures.
Understood the AI-native paradigm — blending cloud-native with generative model architectures — optimizing for performance, modularity, and efficiency.
Delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs.
Driven execution across multiple workstreams, ensuring quality, delivery, and alignment with client outcomes.
See more information on our benefits here: U.S. Employee Benefits | Accenture