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Accenture

Advises businesses and runs their technology

AI Native Software Engineer

AI Application EngineeringUpdated on Nov 10
Full-timeNo salary listedHybrid

About the Job

Requirements

  • Significant software engineering experience in production environments is required; no specific number of years is given.
  • Hands-on experience designing and deploying agentic AI solutions in a production environment is required.
  • Production-depth experience with an agentic orchestration framework such as LangGraph, CrewAI, AutoGen, or an equivalent is required.
  • Direct production-code experience calling LLM APIs such as OpenAI, Anthropic, or Vertex AI, including provider abstraction, token management, and latency and cost tradeoffs, is required.
  • Experience owning RAG pipelines, including embeddings, chunking strategy, vector databases, and context engineering, is required.
  • LLMOps fundamentals, including evaluation harness design, prompt versioning, and production observability, are required.
  • Cloud-native engineering maturity with Kubernetes, Docker, microservices, serverless, CI/CD, and infrastructure as code using Terraform or Helm is required.
  • Strong Python skills are required; Java or an equivalent backend language is acceptable. Production debugging and observability experience are required.
  • Experience managing and developing a team of engineers, performance-managing them, setting individual development plans, and conducting career conversations is required.

Responsibilities

  • Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability.
  • Define RAG pipeline standards across engagements by establishing chunking and embedding strategies, setting quality benchmarks, and ensuring metric-backed tradeoff decisions are documented and transferable.
  • Set multi-LLM integration standards, defaulting to vendor-agnostic architecture and establishing fallback routing and cost governance across providers including OpenAI, Anthropic, Vertex AI, and open-source models.
  • Own LLMOps at programme scale, including evaluation strategy, prompt governance, observability tooling standards, safety monitoring, and cost controls across concurrent systems.
  • Lead client engineering engagements at senior level by facilitating architecture design sessions, leading proof-of-concept delivery, and aligning client technology leadership with delivery teams.
  • Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp-up time on new client engagements.
  • Own the measurement framework for agentic system quality by defining accuracy, latency, safety, and cost metrics, and presenting programme-level AI impact in business terms to senior client stakeholders.

Desired Qualifications

  • Quality of experience is weighted more heavily than years of experience; candidates who have shipped three production agentic systems in four years are preferred over generalists with passive AI exposure.

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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