AI Engineer - FDE Software Engineering Sr. Manager

Accenture•Milwaukee, NY

6d•Hybrid

About The Position

We Are Accenture is a leading solutions and services company that helps the world’s leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed across the enterprise, bringing together the talent of our approximately 800,000 people, our proprietary assets and platforms, and deep ecosystem relationships. Our strategy is to be the reinvention partner of choice for our clients and to be the most client-focused, AI-enabled, great place to work in the world. Through our Reinvention Services we bring together our capabilities across strategy, consulting, technology, operations, Song and Industry X with our deep industry expertise to create and deliver solutions and services for our clients. Our purpose is to deliver on the promise of technology and human ingenuity, and we measure our success by the 360° value we create for all our stakeholders. Visit us at www.accenture.com . There is never a typical day at Accenture, but that’s why we love it here! This is an extraordinary chance to work in a digitally driven, innovation-led environment where you can help clients in their own reinvention journey. As you build a rewarding career at Accenture, you’ll have the opportunity to work with leading-edge technologies and take on some of the coolest projects you can imagine. You are: 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. 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 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)

Nice To Haves

Served as an Agentic AI Engineer in an Enterprise environment
Additional AI certifications or agentic tool experience is a plus.
Defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures.
Understand 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

Responsibilities

Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.
Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.
Tailor and deploy agentic applications across verticals — e.g., finance, healthcare, retail — addressing domain-specific processes via intelligent automation.
Conduct design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
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 to influence internal assets and client roadmaps.