AI Incubation Forward Deployment Engineer

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 AI-assisted engineering practices. 

AI Operations & Development Productivity

• AI Solution Operations (LLMOps): Operate and continuously optimize incubated and piloted AI solutions, including model and prompt version management, monitoring of quality, latency, cost, and usage, incident handling, and model upgrades, ensuring stable, reliable, and cost-effective AI services. 

• AI for IT Operations (AIOps): Apply AI to IT operations, such as intelligent alerting, log and incident analysis, root cause analysis, automated ticket triage, and knowledge-based self-service support, to improve system stability and operational efficiency. 

• AI-Assisted Software Development: Introduce and promote AI coding assistants and agentic development tools (e.g., GitHub Copilot, Cursor, Claude Code) across the software development lifecycle, covering requirements analysis, code generation, code review, testing, and documentation. 

• Engineering Productivity Improvement: Define best practices, guidelines, and guardrails for AI-assisted development and operations; drive adoption across development and support teams; and measure and report productivity gains (e.g., delivery lead time, code quality, incident resolution time). 

JOB REQUIREMENTS & COMPETENCIES

Job requirements

Academic / Professional qualification

• Bachelor's degree or above in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related disciplines; a master's degree is preferred. 

Technical / skill training

• Strong hands-on software engineering skills in Python (TypeScript or Java is a plus), with experience building and deploying applications, APIs, and cloud-native services (e.g., Docker, Kubernetes, CI/CD) on Azure, AWS, or China cloud platforms. 

• In-depth knowledge of generative AI technologies, including LLMs, prompt and context engineering, RAG, embeddings and vector databases, fine-tuning, AI agents and multi-agent frameworks (e.g., LangChain/LangGraph, AutoGen, Semantic Kernel), MCP, and model evaluation. 

• Practical knowledge of LLMOps / MLOps, AI observability and evaluation, and AI security, plus familiarity with enterprise integration (e.g., M365, Copilot, low-code platforms, enterprise APIs) and agile delivery practices, as well as hands-on experience with AI coding assistants and AIOps tools. 

Working experience

Essential

• 6+ years of experience in software engineering, AI/ML, or solution architecture, including at least 2 years of hands-on generative AI / LLM application development. 

• Proven track record of taking AI innovations from 0 to 1, building PoCs, MVPs, and pilots that delivered measurable business value in an enterprise environment. 

• Experience in a forward-deployed, solution engineering, or customer-facing technical role, working directly with business users to translate needs into working technical solutions. 

• Experience collaborating with development teams to transition prototypes into production-grade solutions, including architecture design, handover, and ongoing technical guidance. 

Desirable

• Experience in pharmaceutical, life sciences, or other regulated industries, with exposure to GxP, CSV, data integrity, PIPL, and generative AI regulatory requirements. 

• Experience with both the China AI ecosystem (e.g., Qwen, DeepSeek, China cloud AI services) and global AI platforms (e.g., Azure OpenAI, Copilot) in a multinational matrix organization. 

• Experience in an innovation lab, startup, or AI incubation program, with a portfolio of AI prototypes or products built from scratch. 

Language ability

• Fluent in English and Mandarin, with strong written and verbal communication skills to work effectively with global teams across time zones, business stakeholders, and external partners. 

Others

• Strong curiosity and learning agility; passionate about emerging AI technologies and able to quickly learn, experiment, and turn new technology into business value. 

• Entrepreneurial, builder mindset with strong ownership, structured problem-solving, and storytelling skills; able to explain complex AI concepts to non-technical stakeholders and influence without direct authority. 

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.