# AI Incubation Forward Deployment Engineer at AstraZeneca

AstraZeneca is hiring an AI Incubation Forward Deployment Engineer. Level rates it Builds AI ●●●●; you can [apply on AstraZeneca](https://jobsbylevel.com/go/e6a731cd-7c1d-40b2-87fa-e6ff6f75c76b?utm_medium=ai-agent).

AI Level 4, AI centrality 85 out of 100. China.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 85 out of 100)
- Location: China
- Posted: October 29, 2026
- Apply: https://jobsbylevel.com/go/e6a731cd-7c1d-40b2-87fa-e6ff6f75c76b?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-incubation-forward-deployment-engineer-at-astrazeneca-0979aa

## Prepare for this job

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## Description

DUTIES & RESPONSIBILITIES AI Technology Research & Exploration AI Technology Radar: Continuously track the latest developments in AI, including LLMs, multimodal models, AI agents and multi-agent frameworks, RAG, Model Context Protocol (MCP), and AI coding tools; maintain an AI technology radar for AZ China and share regular insights and trend briefings. Framework & Product Evaluation: Research, benchmark, and evaluate AI frameworks, platforms, and products (e.g., LangChain/LangGraph, LlamaIndex, Dify, Semantic Kernel, Microsoft Copilot Studio, Azure OpenAI, and leading China LLMs such as Qwen and DeepSeek), assessing capability, maturity, cost, security, compliance, and availability in China. Hands-on Experimentation: Set up AI sandboxes and run rapid technical experiments to validate new technologies against enterprise scenarios, documenting findings, limitations, and lessons learned. Technology Recommendations: Provide recommendations on AI technology selection and adoption to enterprise architecture and IT leadership, aligned with AZ Global AI strategy, platforms, and standards. Business-Driven AI Opportunity Discovery Forward-Deployed Business Engagement: Work side by side with business functions (e.g., Commercial, Medical, R&D, Supply Chain, Finance, HR, Compliance) to understand their workflows, pain points, and data, and uncover where AI can create meaningful impact. Use Case Identification & Prioritization: Identify and shape high-value AI use cases; assess business value, technical feasibility, data readiness, and risk; and maintain a prioritized AI innovation pipeline. Alignment with Enterprise AI Direction: Align the incubation portfolio with the AZ China and Global enterprise AI roadmap, reusing global AI platforms and assets as the default and localizing for China only where necessary. Value Hypothesis & Success Criteria: Define clear value hypotheses, KPIs (e.g., productivity gains, cycle-time reduction, quality improvement, user adoption), and go/no-go criteria for each innovation before incubation starts. AI Innovation Incubation (0 to 1) Rapid Prototyping & PoC: Design and build PoCs and MVPs hands-on within weeks, such as AI agents, RAG-based knowledge assistants, intelligent document processing, and AI-enabled business workflows. Solution Design & Architecture: Design end-to-end AI solution architectures, covering model selection, prompt and context engineering, retrieval, tool and agent orchestration, and integration with enterprise systems and data (e.g., SSO, APIs, M365, Workmate). Pilot & Iteration: Deploy pilots with real users, collect feedback and usage data, and iterate quickly to refine user experience, accuracy, and business fit. Evaluation & Measurement: Establish evaluation frameworks and datasets to measure answer quality, accuracy, hallucination, latency, and cost, and quantify the business value delivered by each pilot. Go/No-Go Decisions & Reporting: Present incubation outcomes to business and IT stakeholders and recommend whether to scale, pivot, or stop each innovation based on evidence. AI Innovation Scale-up & Delivery Enablement (1 to 10) Transition to Development Teams: Hand over validated innovations to development teams with complete solution architecture, code, documentation, evaluation sets, and known risks, ensuring a smooth transition from prototype to product. Co-Development & Technical Guidance: Support and guide development teams through engineering hardening and productionization, including performance, scalability, security, observability, and LLMOps practices, and step in hands-on to solve critical technical challenges. Reusable Assets & Reference Patterns: Build and maintain reusable AI components, templates, prompt libraries, agent tools and connectors, and reference architectures to accelerate future AI delivery. AI Capability Building: Share knowledge through tech talks, workshops, and playbooks; upskill development teams and the wider AI community; and promote

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