AI Forward Deployed Engineering

Accenture•Vancouver, BC

7d•Hybrid

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 Forward Deployed Engineering with a minimum of 5 years of experience building cloud-native solutions, and experience 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.

Requirements

Bachelor’s degree in Computer Science, Engineering, or equivalent; additional AI certifications or agentic tool experience is a plus.
Minimum of 5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 year designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows)
Minimum of 1 years experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
Minimum of 5 years experience programming in Python, Java, or equivalent; familiarity with evaluation tooling, logging, monitoring, and agent observability.

Nice To Haves

You’ve served as a Forward Deployed Engineer or 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.

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.