位于上海市, c23的 12 个优质pilot职位（正在招聘！）

AI Native Software Engineering Sr. Manager
Accenture —上海市
- 全职
10天
Senior Solutions Architect, CSP System
NVIDIA —上海市
- 全职
6天
Sr. Process Engineer
Corning —上海市
7 小时
ANALYST DIGITAL MKTG IV
TE Connectivity —上海市
12 小时
Logistics Engineer Remanufacturing
Volvo Group —上海市
Senior Engineer, Cloud Services (f/m/d)
Porsche Engineering Group GmbH —上海市
- 全职
Engineering Section Manager, COC
Corning —上海市
Lead - Commercial Training Design
On AG —上海市
R&D Product Development Engineer
TE Connectivity —上海市
Sr. Scientist, Surface Technology
Corning —上海市浦东新区
Category Manager(Cable&Connector&Electronics)
ABB Robotics (Shanghai) Limited —上海市
快速申请
13天
Senior Hardware Engineer
Magna International —上海市
快速申请
14天

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AI Native Software Engineering Sr. Manager

Accenture -
上海市

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职位详情

全职
10 天前

完整职位描述

Role Description

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.

This role sits at the heart of the AI engineering talent market — demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.

Key Responsibilities

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

Define RAG pipeline standards across engagements: 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 by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models

Own LLMOps at programme scale: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems

Lead client engineering engagements at senior level — 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 and reduce ramp-up time on new client engagements

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 — non-negotiable

Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level

Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs

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

Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure

8-10 years of experience leading software engineering teams: overseeing delivery, allocating resources across workstreams, and owning the professional development of direct reports"

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在上海市, c23的pilot个职位

AI Native Software Engineering Sr. Manager

Senior Solutions Architect, CSP System

Sr. Process Engineer

ANALYST DIGITAL MKTG IV

Logistics Engineer Remanufacturing

Senior Engineer, Cloud Services (f/m/d)

Engineering Section Manager, COC

Lead - Commercial Training Design

R&D Product Development Engineer

Sr. Scientist, Surface Technology

Category Manager(Cable&Connector&Electronics)

Senior Hardware Engineer

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