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Accenture

Advises businesses and runs their technology

Forward Deployed AI Engineer - Associate Manager

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeCA$100.3k - CA$216.7k /yr

About the Job

Requirements

  • A bachelor's degree or college diploma in a related field is required.
  • At least 7 years of engineering experience with cloud-native systems, including APIs, microservices, containerization, and serverless architectures.
  • At least 5 years of software engineering experience using Java, TypeScript, JavaScript, or Python, and at least 5 years integrating APIs and building distributed production systems.
  • At least 1 year of experience designing and deploying agentic solutions—including agents, orchestration, context engineering, retrieval-augmented generation, and workflows—in production environments.
  • At least 1 year of hands-on experience applying AI-native and agentic software development practices across the software development lifecycle, including specification-driven development and AI coding agents for software design, coding, testing, debugging, modernization, documentation, and delivery.
  • At least 1 year of experience with AI platforms such as OpenAI, Claude, Vertex AI, or open-source models, including building abstraction layers for multi-provider pipelines and working with enterprise AI platforms such as Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir.
  • At least 7 years of experience leading, managing, and developing software engineering teams, including delivery oversight, resource allocation, performance management, development planning, and professional development of direct reports.
  • At least 5 years of client-facing communication and collaboration experience, including leading technical discussions, workshops, or delivery sessions under ambiguity, and experience leading executive workshops, architecture design sessions, or cross-functional technical engagements.
  • At least 5 years of experience translating high-level business needs into concrete technical designs and implementations in large enterprise-wide organizations or a professional services firm.
  • Demonstrated end-to-end delivery ownership in a client-embedded environment, including experience leading enterprise AI deployments across multi-stakeholder environments.
  • Experience defining enterprise AI architecture across identity, data, security, governance, and multi-system integrations.
  • Experience working directly with senior stakeholders such as CTOs, CFOs, and CISOs on AI strategy and investment decisions.

Responsibilities

  • Lead enterprise AI platform deployments across complex, multi-stakeholder client environments, owning the full program from architecture through adoption and ongoing iteration, with accountability for quality, time-to-value, reliability, adoption velocity, scalability, and commercial metrics across multiple concurrent workstreams.
  • Drive rapid experimentation by turning ambiguous business problems into working production systems in days or weeks through hands-on prototyping, iteration, and deployment of AI-enabled workflows, agents, copilots, and automations in operational environments.
  • Architect and govern enterprise AI solutions across identity, data, security, governance, platform layers, and multi-system workflow integration at program scale, including prompt and workflow design, API integrations and orchestration, enterprise knowledge integration, and privacy considerations; provide technical leadership and best practices for responsible, secure, and scalable AI deployment.
  • Shape AI reinvention strategy for client CTOs, CFOs, and CISOs by building value architecture, return-on-investment backlogs, use-case prioritization frameworks, and multi-year AI adoption roadmaps.
  • Codify delivery learnings, failure patterns, and engineering standards into reusable reinvention blueprints, patterns, and accelerators; define and publish these assets to scale across client engagements, grow the forward-deployed engineering practice, and enable future forward-deployed engineers.
  • Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams.
  • Work directly with clients and internal teams to understand business problems, shape AI use cases, and define technical solutions from ambiguous or evolving requirements; serve as the technical bridge between product, engineering, data, and business stakeholders to ensure solutions are technically sound, usable, and aligned with business value.
  • Design and implement end-to-end AI solutions, integrating large language models and AI services such as OpenAI, Anthropic, and Gemini with enterprise platforms including SAP, ServiceNow, Salesforce, data platforms, and internal systems.

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

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