# AI Engineer (Agentic/Applied)

**Company**: Accenture
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Consulting

**Apply**: https://accenture.wd103.myworkdayjobs.com/en-US/AccentureCareers/job/Shanghai/AI-Native-Software-Engineering-Consultant_R00341058?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_aeed4a32-8f1

## Description

You build production agentic architectures running inside real client organizations, not demos or prototypes. As an AI Engineer (Agentic/Applied), you will design, build and deploy production-grade agentic AI systems across the full enterprise technology stack.

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 engineering 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 LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling, cost and safety monitoring

- Embed directly with client engineering teams to design, prototype and deploy agentic solutions

- Build reusable patterns, accelerators and playbooks that scale beyond individual client engagements

- 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:

- 5+ years of software engineering experience in production environments

- Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment

- Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen or equivalent

- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code

- RAG pipeline ownership: embeddings, chunking strategy, vector databases and context engineering

- LLMOps fundamentals: eval harness design, prompt versioning and production observability

- Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD and IaC (Terraform or Helm)

- Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience

## Skills

### Required
- agentic AI systems
- multi-agent orchestration
- RAG pipelines
- LLM providers
- LLMOps
- Kubernetes
- Docker
- Python
- Java

### Nice to have
- LangGraph
- CrewAI
- AutoGen
- OpenAI
- Anthropic
- Vertex AI

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Source: [Apply at accenture.wd103.myworkdayjobs.com](https://accenture.wd103.myworkdayjobs.com/en-US/AccentureCareers/job/Shanghai/AI-Native-Software-Engineering-Consultant_R00341058?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)