默克药厂
Principal Agentic AI Engineer
Principal Agentic AI Engineer
发布于 3 小时前普通员工/个人贡献者
AI 估算 · 5.5–8.5万 · AI 参考估算
Jerusalem, Jerusalem, Israel
高级经验
硕士
全职员工仅现场办公人工智能
人工智能
Agentic Ai
Api Integration
Containerization
Machine Learning
Prompt Engineering
值不值得投
78综合评分
综合评分 78
A highly impactful, frontier-tech AI role in a global healthcare leader, offering strong purpose and growth potential, though specific lifestyle and compensation details remain undisclosed.
从薪资福利、成长发展、工作生活和使命价值四个维度综合评估，方便横向比较。
薪资怎么样
7万
比人工智能中位数高
发布情况
新岗位 · 今天发布
公司情况
这家公司招聘很活跃
60 个在招
市场机会
选择面较广
人工智能 · 约 1560 个在招
职位详情
关于这个职位
As a Principal Agentic AI Engineer at Merck, you will design and deliver agentic AI solutions using large language models to transform pharmaceutical R&D workflows. You will collaborate with scientists and engineers to build AI agents capable of reasoning, planning, and executing complex tasks, driving innovation in healthcare and life sciences.
最低要求
Master’s degree or PhD in computer science, artificial intelligence, machine learning, data science, software engineering, or a related quantitative discipline.
Minimum of 5 years professional experience in AI engineering, machine learning, natural language processing, or the development of AI-enabled applications.
Hands-on experience developing applications using large language models, generative AI, retrieval-augmented generation, or agentic AI approaches.
Understanding of core agentic-system concepts, including reasoning and planning, tool use, workflow orchestration, memory, state management, and human-in-the-loop interaction.
Practical experience with prompt and context engineering, document retrieval, embedding-based search, structured data integration, and grounding model outputs in trusted sources.
Strong programming skills in Python and experience building modular, tested, and maintainable software.
Familiarity with APIs, model-serving interfaces, databases, version control, automated testing, containerization, and cloud-based development environments.
Experience evaluating AI systems using quantitative metrics, structured test cases, model comparisons, and feedback from users or subject-matter experts.
Understanding of the limitations and failure modes of large language models and ability to implement appropriate controls, fallback mechanisms, and evaluation approaches.
Comfortable working iteratively with users, moving rapidly from an initial problem statement to a functioning prototype and progressively improving it based on evidence and feedback.
Strong analytical and problem-solving skills, motivated by applying emerging AI technologies to complex scientific and business challenges.
Ability to collaborate effectively across scientific, engineering, product, and IT teams and communicate technical concepts clearly to diverse audiences.
工作职责
Design, prototype, and deliver agentic AI solutions within the Advanced Data Science team in the Data Sciences organization.
Apply large language models and emerging agentic AI methods to transform complex scientific and operational workflows across pharmaceutical R&D.
Translate use-case requirements into functioning AI applications in collaboration with scientists, business stakeholders, software engineers, and ML experts.
Develop agents capable of reasoning over enterprise knowledge, interacting with scientific tools and data sources, executing multistep tasks, and collaborating with users through human-in-the-loop workflows.
Experiment with and optimize approaches for agent design, reasoning and planning, context engineering, retrieval-augmented generation, memory, tool use, and workflow orchestration.
Evaluate alternative models, prompts, retrieval strategies, and agent configurations using structured testing and user feedback to improve solution quality, reliability, latency, and cost.
Contribute across the complete use-case delivery lifecycle, from rapid prototyping and user demonstrations through validation and production readiness.
Develop modular and reusable components while ensuring solutions include appropriate error handling, traceability, evaluation, and safeguards.
Work in close partnership with software engineering and IT teams to integrate agentic workflows with approved enterprise platforms, data services, and applications.
Stay at the forefront of developments in foundation models, agentic systems, and AI engineering, rapidly assessing emerging approaches and translating them into practical solutions.
优先资格
Experience in pharmaceutical R&D, healthcare, life sciences, or another regulated environment.
AI 洞察
优缺点分析
优点
- Work at the cutting edge of Agentic AI and LLMs, applying them to high-impact pharmaceutical R&D challenges.
- Join a prestigious global company with a strong mission to improve and prolong lives through innovative healthcare solutions.
- Collaborate with diverse, cross-functional teams of scientists and engineers, offering rich opportunities for learning and networking.
- Navigating the complexities of integrating advanced AI agents into highly regulated pharmaceutical environments with strict compliance requirements.
- Managing the inherent unpredictability and failure modes of LLMs while ensuring scientific accuracy and reliability in R&D workflows.
- Highly analytical and innovative AI engineers who are passionate about applying cutting-edge agentic AI technologies to solve complex scientific problems in the healthcare and life sciences sector.
缺点 / 挑战
暂无明显挑战项
角色解读
- Progress from leading complex AI prototypes to driving the strategic adoption of agentic AI across global R&D operations.
- Transition into AI architecture or leadership roles within the Data Sciences organization, shaping the future of AI in healthcare.
- Design and build agentic AI solutions using LLMs to automate and enhance complex pharmaceutical R&D workflows.
- Collaborate with scientists and engineers to develop AI agents that can reason, plan, and interact with enterprise data and tools.
- Evaluate and optimize AI models, prompts, and retrieval strategies to ensure high reliability, low latency, and cost-effectiveness.
- Deep expertise in Python, LLMs, Agentic AI, RAG, and prompt/context engineering.
- Strong understanding of AI system evaluation, failure modes, and implementing robust fallback mechanisms.
- Experience with cloud environments, APIs, containerization, and integrating AI workflows into enterprise platforms.
申请策略
- Tailor your application to emphasize your ability to translate complex scientific requirements into robust, production-ready AI solutions.
- Highlight specific projects where you designed and deployed agentic AI or RAG systems, emphasizing the business or scientific impact.
- Showcase your expertise in Python, LLM integration, and evaluating AI systems using quantitative metrics and structured testing.
- Demonstrate experience working in regulated environments or collaborating with domain experts (e.g., scientists, healthcare professionals).
- Deepen your knowledge of advanced agentic frameworks, multi-agent orchestration, and context engineering techniques.
- Familiarize yourself with pharmaceutical R&D workflows and the regulatory constraints of deploying AI in healthcare.
面试指南
- Use the STAR method (Situation, Task, Action, Result) to structure your responses, focusing on the technical challenges and the measurable impact of your solutions.
- When discussing LLM limitations, emphasize your proactive approach to evaluation, implementing guardrails, and designing fallback mechanisms.
- Can you walk us through a complex agentic AI system you designed? How did you handle reasoning, planning, and tool use?
- How do you evaluate the reliability and accuracy of an LLM-based agent in a domain where errors can have significant consequences?
- Describe your approach to integrating an AI agent with existing enterprise data sources and ensuring data security and compliance.
- How do you stay updated with the rapid advancements in foundation models and agentic systems, and how do you decide which new techniques to adopt?
- Review the latest research and practical implementations in Agentic AI, RAG, and multi-agent systems to discuss current trends and best practices.
- Prepare concrete examples of how you have collaborated with non-technical stakeholders or domain experts to define requirements and iterate on AI prototypes.
职位点评
薪资福利75
成长发展85
工作生活65
使命价值90
薪资福利
75中等
The role offers a competitive compensation package typical for a Principal-level position, though specific salary details are not disclosed in the job description.
薪资信号未披露（AI估算：5.5千–8.5万/月）
成长发展
85较高
The position provides excellent opportunities to work with cutting-edge AI technologies and offers clear pathways for personal development and global career advancement.
技术前沿前沿/新兴技术
技术栈Large Language Models、Agentic AI、RAG、Python、Prompt Engineering
成长机会personal development、career advancement
业务类型ambiguous
工作生活
65中等
The company promotes a flexible working culture, but the exact work mode and work-life balance expectations are not explicitly detailed in the job description.
工作模式未明确
办公地点未明确
加班情况未提及（无法判断）
工作生活平衡flexible working culture
使命价值
90较高
The role is deeply aligned with a strong social mission to improve and prolong lives through innovative healthcare solutions, offering a high sense of purpose.
行业发展高速增长赛道
社会影响正向社会影响力较高
使命信号enrich people's lives、help create, improve and prolong lives、champion human progress
创新程度积极采用新技术
默克药厂 的其他在招职位
Pharmaziepraktikum gemäß §4 Approbationsordnung für Apotheker im Bereich Qualitätssicherung (m/w/d)
默克药厂 · Darmstadt, Hessen, GermanyAI 估算 · 8千–1.2万Pharmaceutical Manufacturing Technician Overnight Weekday Shift
默克药厂 · Verona, Wisconsin, United StatesAI 估算 · 3.1–5.1万Senior Container Engineer // Senior Mechanical Engineer (Container)
默克药厂 · 高雄市AI 估算 · 1.5–2.5万
相似职位推荐
Watch Jobs