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AI Native Software Engineer

at Accenture

Back to all Data Science / AI / ML jobs
Accenture logo
Consultancies

AI Native Software Engineer

at Accenture

Mid LevelNo visa sponsorshipData Science/AI/ML

Posted 6 days ago

No clicks

Compensation
Not specified USD

Currency: $ (USD)

City
Not specified
Country
United States

Join Accenture as an AI Native Software Engineer, building cloud-native AI agents and agentic workflows for enterprise clients. You’ll embed directly with customers to define use cases, rapidly prototype, and deploy robust agentic workflows that scale across modern infrastructures. The role spans AI platform integration, cloud-native engineering, and domain-specific workflows across industries, shaping how enterprises adopt AI-native engineering. Depending on level, you may lead design and mentor others or own key technical areas end-to-end, while contributing reusable patterns and best practices.

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.

You are

An AI Native Engineer with a strong foundation in building cloud-native solutions and hands-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure.

Depending on level, you’ll either:

  • Lead the design and delivery of complex agentic solutions and mentor/coach other engineers or

  • Serve as an individual contributor owning key technical areas end to end

  • In all cases, you’ll help shape the playbook for how enterprises adopt and scale AI-native engineering.

The Work

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be net-new platforms and systems that need to be stitched together in our clients’ environments alongside our ecosystem partners.

Agent Architecture & Engineering

  • 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.

AI Platform Integration

  • 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.

Cloud-Native Engineering

  • 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.

Domain-Specific Workflows

  • 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.

Client Engagement

  • 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.

Measure & Improve

  • 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.

Knowledge Sharing

  • 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.

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

AI Native Software Engineer

at Accenture

Back to all Data Science / AI / ML jobs
Accenture logo
Consultancies

AI Native Software Engineer

at Accenture

Mid LevelNo visa sponsorshipData Science/AI/ML

Posted 6 days ago

No clicks

Compensation
Not specified USD

Currency: $ (USD)

City
Not specified
Country
United States

Join Accenture as an AI Native Software Engineer, building cloud-native AI agents and agentic workflows for enterprise clients. You’ll embed directly with customers to define use cases, rapidly prototype, and deploy robust agentic workflows that scale across modern infrastructures. The role spans AI platform integration, cloud-native engineering, and domain-specific workflows across industries, shaping how enterprises adopt AI-native engineering. Depending on level, you may lead design and mentor others or own key technical areas end-to-end, while contributing reusable patterns and best practices.

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.

You are

An AI Native Engineer with a strong foundation in building cloud-native solutions and hands-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure.

Depending on level, you’ll either:

  • Lead the design and delivery of complex agentic solutions and mentor/coach other engineers or

  • Serve as an individual contributor owning key technical areas end to end

  • In all cases, you’ll help shape the playbook for how enterprises adopt and scale AI-native engineering.

The Work

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be net-new platforms and systems that need to be stitched together in our clients’ environments alongside our ecosystem partners.

Agent Architecture & Engineering

  • 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.

AI Platform Integration

  • 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.

Cloud-Native Engineering

  • 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.

Domain-Specific Workflows

  • 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.

Client Engagement

  • 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.

Measure & Improve

  • 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.

Knowledge Sharing

  • 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.

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

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