AI Engineering & Operations Analyst

该职位来源于猎聘 Role Purpose The Enterprise AI Engineering &

• Operations Analyst supports the implementation, operation, and continuous help to build General Mills China’s enterprise AI capabilities. The role provides hands-on technical execution and operational support across the Enterprise AI Portal, AI platforms, models, integrations, and related enterprise AI services. The role combines enterprise AI technical knowledge with engineering delivery and operational management. It is responsible for coordinating technical implementation, validating solution quality, managing engineering partners, and supporting reliable platform operations. The role is expected to have sufficient engineering fundamentals to understand implementations, review technical approaches, and troubleshoot issues when needed, while full-time software development is not the primary responsibility.

Key Responsibilities

• Enterprise AI Platform Engineering &
  • Operations

• Support Enterprise AI Portal implementation, enhancement, configuration, and day-to-day technical operations.
• Support enterprise AI platform administration, environment configuration, access enablement, and operational troubleshooting.
• Monitor platform health, service reliability, usage, and technical issues; coordinate resolution with internal teams and vendors.
• Support maintain technical documentation, runbooks, and operational best practices.
• Support technical implementation and validation of AI services, APIs, integrations, automation components, and other lightweight technical solutions, working with engineering vendors and platform teams where appropriate.
• AI Integration &
  • Orchestration

• Support integrations among enterprise AI platforms, LLM services, knowledge services, internal applications, and external AI tools.
• Support and manage API, workflow, Agent, Skill, and orchestration implementation for reusable enterprise AI capabilities.
• Coordinate integration testing and end-to-end technical validation across connected systems.
• Ensure integrations follow established enterprise architecture, security, and engineering standards, and support technical review when needed.
• AI Model &
  • Technology Evaluation

• Evaluate LLMs, multimodal models, AI platforms, Agent frameworks, AIOps, AI FinOps, and emerging enterprise AI technologies.
• Define and coordinate technical benchmarks covering quality, latency, stability, cost, scalability, and operational fit.
• Support model/platform comparison and technical recommendations for enterprise use cases.
• Track relevant AI technology trends and translate them into practical enterprise experiments.
• Proof of Concept &
  • Prototype Validation

• Coordinate technical Proof of Concept (PoC) activities for new AI capabilities and business scenarios.
• Support prototype implementation and technical validation to assess feasibility before scaled implementation.
• Define technical test scenarios, document findings, and identify risks, dependencies, and production-readiness gaps.
• Support transition from prototype to production implementation with engineering partners.
• Engineering Vendor &
  • Delivery Coordination

• Coordinate external engineering vendors and technical implementation activities.
• Clarify technical requirements, review implementation approaches and delivery quality, identify technical risks, and challenge vendor recommendations when necessary.
• Participate in functional, integration and regression testing, UAT support, release validation, and go-live activities.
• Support post-go-live stabilization and continuous technical improvement.
• AI Operations, Cost &
  • Reliability

• Support operational monitoring of enterprise AI services, including model usage, performance, reliability, and cost.
• Support AI FinOps practices such as usage analysis, model/service cost comparison, and optimization opportunities.
• Identify opportunities to standardize and automate recurring AI platform operations.
• Support incident analysis and continuous improvement to reduce operational overhead.
• Technical Enablement &
  • Governance Support

• Organize or support AI technical enablement programs, including AI 301 and other technical training sessions.
• Provide technical guidance to internal users and project teams on approved enterprise AI capabilities.
• Support documentation and evidence preparation for architecture, security, privacy, compliance, and AI governance reviews.
• Promote reusable engineering patterns and technical standards across China enterprise AI initiatives. 工作经历 / 知识、技能和能力：Qualifications and skills: Experience 3–5 years experience in one or more areas:
• AI / data / software engineering or technical delivery
• Enterprise application or platform engineering
• Cloud platform or integration engineering
• AI platform operations / MLOps / LLMOps
• Enterprise system integration or application delivery
• Technical project delivery, vendor management, or production system support Technical Skills Preferred:
• Working knowledge of Python and modern programming concepts, with the ability to understand, review, and troubleshoot code when required
• Good understanding of software engineering fundamentals, including modular design, error handling, debugging, testing, and maintainability
• Good understanding of RESTful APIs, backend services, authentication, asynchronous processing, and third-party system integrations
• Working knowledge of SQL and databases; familiarity with NoSQL, caching, or data processing technologies is a plus
• Familiarity with Linux environments, containers such as Docker, and cloud-native application concepts
• Understanding of Git, CI/CD, code review, testing, and modern software delivery / DevOps practices
  • Azure DevOps experience is preferred

• Good understanding of LLMs, multimodal models, RAG, Agents, workflows, MCP / Skills, and AI orchestration concepts
• Experience with at least one major cloud or enterprise AI platform
  • Alibaba Cloud / Azure experience is preferred

• Ability to understand enterprise system integration, authentication, API security, architecture, and data flows
• Basic knowledge of observability, AIOps, AI FinOps, model evaluation, and performance / cost optimization
• Ability to support technical troubleshooting, system testing, integration testing, release validation, and production operations Competencies
• Strong problem-solving skills with sufficient hands-on technical capability to investigate and validate engineering issues
• Ability to learn and evaluate new AI technologies quickly
• Ability to translate technical topics into clear actions and coordinate effectively with product, business, platform, and engineering partners
• Strong ownership of technical delivery, with the ability to manage vendors and drive issues through resolution
• Self-driven and comfortable managing multiple priorities in a fast-changing environment
• Strong technical judgment, documentation habits, and structured thinking
• Fluent Chinese and good English communication skills
• Intellectual curiosity, creativity, and learning agility 教育程度 / Educational Requirements: Education Bachelor’s or above degree in:

Computer Science

Software Engineering

Information Systems

• Data Science / Artificial Intelligence
• Related engineering or technical disciplines