AI Native Software Engineer
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Key skills for this role
About the Role
Accenture Middle East seeks an AI Native Engineer to design and deploy enterprise-ready agentic workflows. The role requires 3+ years of cloud-native engineering experience and 1+ year of hands-on agentic solution deployment, with strong client-facing skills.
Key Skills for This Role
Responsibilities
- Design and build enterprise ready AI agents incorporating retrieval, orchestration, policy based routing, tool invocation, evaluation harnesses, and lifecycle observability
- Develop and/or extend abstraction layers across AI providers to enable seamless integration and multi provider enablement
- Leverage containerization, microservices, serverless, event driven architectures, CI/CD, and observability stacks to deliver scalable AI native systems
- Tailor and deploy agentic applications across verticals, adapting to domain specific processes and constraints
- Participate in and/or lead design workshops, POCs, and code with sessions to shape data driven agent workflows with stakeholders
- Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness
- Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps
Requirements
- Minimum 3 years of engineering experience with cloud native systems (APIs, microservices, containerization, serverless)
- Minimum 1 year of hands on experience designing and deploying agentic solutions in production or near production environments
- Minimum 1 year of experience with modern AI platforms (OpenAI, Claude, Vertex AI, or open source models)
- Minimum 3 years strong Python, Java or equivalent experience building 12 factor applications + Infrastructure as Code (Terraform, Helm)
- Minimum 3 years of experience in client facing communication and collaboration
- Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience
Full Job Posting
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.
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.
The Work
- You’ll partner directly with client stakeholders — 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.
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.
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