# 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-Manager_R00340980?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_49c2144d-150

## Description

You build production-grade agentic AI systems for enterprise environments. As an AI Engineer (Agentic/Applied), you will design, build, and deploy systems across the full enterprise technology stack, working with client engineering teams and leading technical design sessions.

**Key Responsibilities**

- Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability

- Define RAG pipeline standards: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable

- Set multi-LLM integration standards: vendor-agnostic architecture, fallback routing, and cost governance

- Own LLMOps at programme scale: eval strategy, prompt governance, observability tooling standards, safety monitoring, and cost controls

- Lead client engineering engagements: facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams

- Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice

- Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme-level AI impact in business terms to senior client stakeholders

**Basic Qualifications**

- 8+ 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
- multi-agent orchestration
- RAG pipelines
- LLM APIs
- LangGraph
- CrewAI
- AutoGen
- Kubernetes
- Docker
- Python
- Java

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