AI Native Software Engineering Senior Manager | Associate Director

Accenture•Cleveland, OH

40d

About The Position

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 minimum of 5 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. 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 completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Requirements

Minimum of 10 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Minimum of 7 years of experience with AI platforms - OpenAI, Claude, Vertex AI, plus open-source models - including building abstraction layers to manage multi-provider pipelines.
Minimum of 10 years of experience programming in Python, Java, or equivalent; familiarity with evaluation tooling, logging, monitoring, and agent observability.
Minimum of 10 years of experience deploying to production - CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
Minimum of 10 years of experience in client communication and collaboration, including being capable of leading technical workshops and delivering under ambiguity.
Bachelor's degree in Computer Science, Engineering, or equivalent; additional AI certifications or agentic tool experience is a plus.

Nice To Haves

You've served as an Agentic AI Engineer in an Enterprise environment
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.

Responsibilities

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.

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