AI Native Software Engineering Lead

Accenture•Toronto, ON

3d•Hybrid

About The Position

ARE YOU READY to step up and take your technology expertise to the next level? There is never a typical day at Accenture, but that’s why we love it here! This is an extraordinary chance to begin a rewarding career at Accenture Technology. Immersed in a digitally compassionate and innovation-led environment, here is where you can help top clients shift to the New using leading-edge technologies on the most ground-breaking projects imaginable. Cloud First - Software Engineering is a high performing team to join! We focus on client adoption of disruptive technologies, technology architecture and providing specialized skills related to integration, custom software engineering, testing, application modernization, agile and more. We help our clients with the most complex projects including working in open web platforms, DevOps platforms as well as intelligent Computing and Architecture enhancement. 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

A bachelor's or master's degree in computer science, business administration, commerce or related discipline is required.
Minimum of 10 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 years of experience in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows).
Minimum of 2 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.

Nice To Haves

Served as an Agentic AI Engineer in an Enterprise environment
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.