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Added 13 mins ago
Director, Customer Engagement Platforms
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Toronto, ON
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HI319: Britain's 18th Century (Winter 2027)
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…Laurier Faculty Association (WLUFA) is the exclusive bargaining agent. All applicants are assessed using both the “Appendix H: Assessment of CTF Candidates under 13.6.1” in the collective…
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Accenture

AI Native Developer (Agentic / Applied)

Calgary, AB, Canada
Vancouver, BC, Canada
Ottawa, ON, Canada
Toronto, ON, Canada
Montreal, QC, Canada

Role Location Annual Salary Range British Columbia/Ontario/New Brunswick $100,300 to $216,700 · Full-time

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Job details

Pay: Role Location Annual Salary Range British Columbia/Ontario/New Brunswick $100,300 to $216,700
Job type: Full-time
Posting language: English
Posted: Oct 7, 2026

Original job description

Role Description

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Developer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Developer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client development teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.

This role sits at the heart of the AI development talent market - demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google technical teams and a direct pathway to the Forward Deployed Developer program.

Key Responsibilities

Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability

Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window design and tuning against real quality targets

Integrate and abstract across multiple LLM providers - OpenAI, Anthropic, Vertex AI, and open-source models - with fallback routing, token, cost, and latency management

Implement LLM Ops in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring

Embed directly with client development teams to design, prototype, and deploy agentic solutions - workshops, proofs of concept, code-with sessions, and architecture walkthroughs

Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster

Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms

Basic Qualifications

Required qualifications

7+ years of software development experience in production environments
Minimum of 1 year of experience designing and deploying agentic AI solutions in production environments
Experience with agentic orchestration frameworks, including LangGraph, CrewAI, AutoGen, or equivalent
Experience integrating and calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production systems, including provider abstraction, token management, and cost/latency optimization
Experience building and operating RAG pipelines, including embeddings, chunking strategy, vector search, and context design
Experience implementing LLM Ops practices, including eval harness design, prompt versioning, and observability tooling
Experience with cloud-native systems, including Kubernetes, Docker, microservices, serverless architectures, CI/CD, and infrastructure as code (Terraform or Helm)
Proficiency in Python; experience with Java or equivalent backend language acceptable
Experience debugging and monitoring production systems
Experience deploying multiple production agentic AI systems
Experience defining or standardizing RAG, LLM integration, or LLM Ops practices across projects
Experience working directly with client development teams in collaborative development environments
Experience building reusable frameworks, accelerators, or delivery playbooks
Experience defining and tracking AI system performance metrics such as accuracy, latency, safety, and cost
Bachelor’s degree or college diploma in a related field.
English is required for this position as this role will be aligned to multi-national teams where English is the common language across our North America Market. Due to the significant high volume of interactions with these English-speaking multi-national teams, which is inherent to this position, it is not possible to reorganize the company's activities to avoid this requirement.

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation, based on full-time employment, for roles that may be hired as set forth below. The recruiting efforts for this position are intended to fill a brand new position. The base pay range shown below is intended as a guideline to reflect the majority of offers for this role. It does not represent a maximum limit - in some cases, actual compensation may exceed the range where appropriate.

Information on benefits is here.

Role Location Annual Salary Range British Columbia/Ontario/New Brunswick $100,300 to $216,700

and

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services - creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us at www.accenture.com

Equal Employment Opportunity Statement

We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.

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More about this job

Responsibilities

Design and deploy production-grade agentic AI systems, including multi-agent orchestration and RAG pipelines, within enterprise environments. Collaborate with client teams to build reusable patterns and accelerators while monitoring system performance metrics like accuracy and latency.

Requirements

Requires 7+ years of software development experience with at least 1 year specifically in deploying agentic AI solutions in production. Proficiency in Python and experience with cloud-native systems, LLM APIs, and LLM Ops practices are essential.

Skills

Python
Agentic AI
RAG Pipelines
LLM Ops
LangGraph
CrewAI
AutoGen
Kubernetes
Docker
Microservices
Terraform
Helm
Vector Search
Prompt Engineering
System Architecture
Cloud-native

Visa sponsorship

Not detected in the job text

Keywords

AI Native Developer
Agentic AI
Production-grade
Multi-agent orchestration
RAG
LLM Ops
Python
Kubernetes
Docker
Microservices
Serverless
CI/CD
Terraform
Helm
LangSmith
Braintrust
OpenAI
Anthropic
Vertex AI
Embeddings
Vector search
Enterprise technology stack
Software development
Architecture
Performance metrics
Cloud-native
Infrastructure as code
Prompt versioning
Observability
Consulting

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