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Accenture

Senior AI and Agentic Developer

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 14, 2026
Posting language: English
Posted: Sep 18, 2026

Original job description

We Are
The beginning of a new era that will reshape work and society has begun. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimize and reinvent their business with Data & Advanced AI - backed by a $3B investment and commitment to our people to do industry-defining work. With over 1,600 professionals dedicated to generative AI, leveraging the depth and experience of more than 40,000 AI and data professionals across the company our Generative AI and LLM Center of Excellence brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem.

You Are:

As an Advanced AI, Large Language Model and Agentic AI Developer/Consultant, you will build production-grade AI applications that transform enterprise workflows. You will translate business and product requirements into secure, scalable software components spanning LLM applications, agent orchestration, enterprise knowledge integration, workflow automation, evaluation, observability and AI Ops. You will integrate and customize LLMs with enterprise systems, APIs and data platforms so clients can move from prototypes to measurable business outcomes.

You will be required to maintain a practical understanding of the evolving Gen AI and agentic AI stack, including model selection, prompt and context engineering, advanced RAG, tool/function calling, MCP/A2A-style integration patterns, orchestration frameworks, guardrails, Responsible AI, security and production operations. You will use that knowledge to build AI-enabled applications and agentic workflows that deliver business value.

Position Responsibilities:

Design, build and support end-to-end Generative AI, LLM and agentic AI applications that transform enterprise workflows for specific business needs, following architecture guidance and delivery standards.

Build tool integrations, workflow actions and API connectors that allow agents to safely retrieve information, execute tasks and hand work back to people when judgment or approval is required.

Create automated tests, evaluations and monitoring for prompts, retrieval quality, agent behavior, security controls, latency, cost and production reliability.

Work in agile, cross-functional delivery teams and communicate technical trade-offs clearly to architects, product owners, business SMEs and client stakeholders.

Leverage, customize and implement Gen AI models, orchestration frameworks, retrieval patterns and agentic design patterns to improve the quality, reliability and adoption of AI products, applications and systems.

Develop LLM-based and agentic applications using open source and cloud frameworks such as LangGraph, Semantic Kernel, LangChain, Pydantic, AutoGen, CrewAI, MCP/A2A-enabled tools or equivalent enterprise AI platforms.

Adapt LLMs and agent workflows to improve performance, accuracy, alignment, reliability, safety and business relevance.

Develop and integrate LLMs, agents and AI application components into products, services, enterprise systems, APIs and workflow platforms.

Implement AI applications that comply with Responsible AI, ethical, privacy, security and legal standards, including data protection, consent, access controls, auditability and human-in-the-loop escalation.

Analyze and evaluate Gen AI and agentic system performance using model, workflow and business outcome metrics, and recommend improvements to quality, safety, latency, cost and user trust.

Contribute to the right technology choices for generative AI and agentic solutions across models, frameworks, cloud services, data platforms, integration layers, observability tools and security controls.

Design and prototype reusable components for LLM and agentic solution patterns, including prompts, context pipelines, memory, tool use, workflow handoffs, human approvals, evaluations and integration adapters.

Build components of LLM and agentic AI solutions that address Responsible AI, privacy, cybersecurity, data governance, auditability, resiliency and regulatory expectations.

Implement LLMOps and AgentOps practices for operationalization, including CI/CD, automated evaluation, prompt and model lifecycle management, monitoring, incident response, cost controls and continuous improvement.

Collaborate with business process owners, product teams, designers, data and platform engineers, security, legal/privacy, risk, change management and operations teams to identify, prioritize and deliver AI-enabled workflow transformation.

Create and maintain clear technical, integration, evaluation and operations documentation that helps cross-functional teams build, troubleshoot, govern and continuously improve AI and agentic solutions.

Apply modern transformer, embedding, retrieval, reasoning and multimodal model capabilities to optimize solution performance, user experience and enterprise workflow outcomes.

Translate advances in transformer architectures and agentic AI into practical software patterns that reshape knowledge work, decision support, service operations and software engineering productivity.

Travel may be required for this role.

What you need:

Minimum of 3 years in AI engineering, machine learning, deep learning and NLP solutions and applications.

Minimum 3 years of experience in designing & deploying AI / Machine Learning solutions using at least one cloud vendor.

Minimum of 2 years of experience in Engineering teams with one or more programming languages and frameworks, such as Python, JavaScript, Java, Spring or GoLang, showcasing a strong command over the technical foundations and mastery of one or more AI Frameworks like LangGraph or Semantic Kernel.

Minimum 1 year of experience architecting and operationalizing LLM-driven and agentic application architecture patterns.

Bachelor’s Degree or completion of college diploma in related field

Demonstrated hands-on experience building Gen AI or agentic AI applications with orchestration frameworks, tool/function calling, RAG, APIs and enterprise integration patterns.

Demonstrated software engineering skills in production environments, including Python or JavaScript/TypeScript, API development, testing, CI/CD, observability and secure coding practices.

Demonstrated ability to collaborate across product, design, data, cloud, security, legal/privacy, business users and operations to deliver AI-enabled workflow change.

Familiarity with enterprise workflow automation, event/message-driven integration, human-in-the-loop design and AI application deployment on cloud platforms.

English is required for this position as this role will regularly interact with English-speaking stakeholders across Canada. Due to the significant high volume of interactions with these English-speaking stakeholders, which is inherent to this position, it is not possible to reorganize the company's activities to avoid this requirement.

Bonus Points if you have

Fine-tuning language models and handling multimodal inputs and outputs.

Experience with agentic workflows, Retrieval-Augmented Generation (RAG), knowledge graphs, vector databases, tool use and data pipelines.

Experience in developing applications for voice, video and text based AI platforms, showcasing versatility and adaptability in diverse environments.

Experience with enterprise workflow transformation, process automation, decision support, contact center/service operations or AI-assisted software engineering use cases.

Experience building evaluation datasets, automated quality checks, guardrails, prompt/version governance, cost optimization and production monitoring for LLM applications.

Experience facilitating demos, discovery sessions or adoption workshops with business users and client stakeholders.

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 $69,450 to $119,450

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More about this job

Responsibilities

Design, build, integrate, and operate production-grade generative AI and agentic applications that automate enterprise workflows, connecting models and agents with APIs, data platforms, and business systems. Establish evaluation, monitoring, security, governance, and operational practices, and collaborate with cross-functional teams and clients to improve solution quality and business outcomes.

Requirements

Requires at least three years of AI/ML engineering experience and cloud-based AI solution deployment, two years of software engineering experience, and one year architecting and operationalizing LLM and agentic applications. A bachelor's degree or related college diploma is required, along with hands-on experience in generative AI, orchestration frameworks, RAG, APIs, production software engineering, and cross-functional delivery.

Skills

Generative AI
Agentic AI
Large Language Models
Python
JavaScript/TypeScript
LangGraph
Semantic Kernel
Retrieval-Augmented Generation
Prompt Engineering
API Development
Cloud Platforms
Machine Learning
LLMOps
AgentOps
CI/CD
Responsible AI

Visa sponsorship

Not detected in the job text

Keywords

Generative AI
Agentic AI
Large Language Models
Machine Learning
Deep Learning
Natural Language Processing
Python
JavaScript
TypeScript
Java
Go
LangGraph
Semantic Kernel
LangChain
AutoGen
CrewAI
Retrieval-Augmented Generation
Vector Databases
Knowledge Graphs
Prompt Engineering
Tool Calling
Model Context Protocol
API Integration
Cloud Platforms
LLMOps
AgentOps
CI/CD
Responsible AI
Human-in-the-Loop
Observability

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