A

Accenture

Advises businesses and runs their technology

Senior AI and Agentic Developer

AI Application EngineeringConfirmed live in the last 24 hours
Full-time$69.5k - $119.5k /yr

About the Job

Requirements

  • Minimum of 3 years of experience in AI engineering, machine learning, deep learning, and natural language processing solutions and applications.
  • Minimum of 3 years of experience designing and deploying artificial intelligence or machine learning solutions using at least one cloud vendor.
  • Minimum of 2 years of experience in engineering teams using one or more programming languages and frameworks, such as Python, JavaScript, Java, Spring, or Go, and strong command of technical foundations and mastery of one or more AI frameworks such as LangGraph or Semantic Kernel.
  • Minimum of 1 year of experience architecting and operationalizing large language model-driven and agentic application architecture patterns.
  • Bachelor’s degree or completion of a college diploma in a related field.
  • Demonstrated hands-on experience building generative AI or agentic AI applications with orchestration frameworks, tool or function calling, retrieval-augmented generation, APIs, and enterprise integration patterns.
  • Demonstrated software engineering skills in production environments, including Python or JavaScript/TypeScript, API development, testing, continuous integration and delivery, 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 because the role regularly interacts with English-speaking stakeholders across Canada, and the volume of interactions cannot be avoided by reorganizing company activities.

Responsibilities

  • Design, build, and support end-to-end generative AI, large language model, 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 subject-matter experts, and client stakeholders.
  • Leverage, customize, and implement generative AI models, orchestration frameworks, retrieval patterns, and agentic design patterns to improve the quality, reliability, and adoption of AI products, applications, and systems.
  • Develop large language model-based and agentic applications using open-source and cloud frameworks such as LangGraph, Semantic Kernel, LangChain, Pydantic, AutoGen, CrewAI, Model Context Protocol/Agent-to-Agent-enabled tools, or equivalent enterprise AI platforms.
  • Adapt large language models and agent workflows to improve performance, accuracy, alignment, reliability, safety, and business relevance.
  • Develop and integrate large language models, 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 generative 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 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 large language model and agentic solution patterns, including prompts, context pipelines, memory, tool use, workflow handoffs, human approvals, evaluations, and integration adapters.
  • Build components of large language model and agentic AI solutions that address responsible AI, privacy, cybersecurity, data governance, auditability, resiliency, and regulatory expectations.
  • Implement large language model operations and agent operations practices, including continuous integration and delivery, 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 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.

Desired Qualifications

  • Fine-tuning language models and handling multimodal inputs and outputs.
  • Experience with agentic workflows, retrieval-augmented generation, knowledge graphs, vector databases, tool use, and data pipelines.
  • Experience developing applications for voice-, video-, and text-based AI platforms.
  • 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 large language model applications.
  • Experience facilitating demos, discovery sessions, or adoption workshops with business users and client stakeholders.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

Recently Posted Jobs

Explore more opportunities at Accenture

See more jobs