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# AI Engineer - FDE Software Engineering Sr. Manager

- **Company:** Accenture
- **Location:** San Diego, CA, United States
- **Experience:** Expert
- **Salary:** $132,500.0 - $302,400.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Abstraction Layers, Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Cloud Engineering, Continuous Integration, Software Debugging, Graph Database, Python (Programming Language), Systems Development Life Cycle, OpenAI, Software Engineering, Data Logging, Retrieval-Augmented Generation, Open-source Models, Prompt Engineering, AI Coding Agents, Agentic-AI, Event Driven Architecture, Containerization, AI Platforms, Kubernetes, Data Analytics, Claude, Terraform, Serverless Computing, Docker, Microservices
- **Published:** October 26, 2026
- **Apply:** https://www.beyondsandiego.com/job.asp?id=3468026658&tx=DT11505UTD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

An AI Native Engineer with a minimum of 3 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You'll help shape the playbook for how enterprises adopt and scale AI-native engineering globally., * Minimum 10 years' experience with end-to-end software engineering and SDLC expertise

 * Minimum 8 years' experience in the following:
 * Programming in Python, Java, or equivalent; familiarity with evaluation tooling, logging, monitoring, and agent observability.
 * Deploying to production - CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
 * With client communication and collaboration, including being capable of leading technical workshops and delivering under ambiguity.
 * Minimum of 3 years' experience in the following:
 * Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
 * With AI platforms - OpenAI, Claude, plus open-source models - including building abstraction layers to manage multi-provider pipelines.
 * Minimum of 2 years of hands-on experience designing and delivering Agentic AI solutions
 * Minimum of 1.5 years' experience in the following:
 * With semantic models, ontologies, knowledge graphs, or enterprise knowledge
 * Using coding agents and AI-assisted software development
 * Minimum of 1 year expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments
 * Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate Degree, must have minimum 6 years work experience), * You've served as an Agentic AI Engineer in an Enterprise environment
 * Additional AI certifications or agentic tool experience is a plus.
 * You've defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures.
 * You understand the AI-native paradigm - blending cloud-native with generative model architectures - optimizing for performance, modularity, and efficiency.
 * You've delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs

## Description

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 completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners., * Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

 * AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.
 * Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.
 * Domain-Specific Workflows: Tailor and deploy agentic applications across verticals - e.g., finance, healthcare, retail - addressing domain-specific processes via intelligent automation.
 * Client Engagement: Conduct design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
 * Measure & Improve: Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness
 * Knowledge Sharing: Craft reusable patterns, documentation, and best practices to influence internal assets and client roadmaps.

Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.