默克药厂
Senior Agentic AI Engineer
Senior Agentic AI Engineer
发布于 3 小时前普通员工/个人贡献者
AI 估算 · 4–7万 · AI 参考估算
Jerusalem, Jerusalem, Israel
中级经验
硕士
全职员工仅现场办公人工智能
人工智能
Agentic Ai
Machine Learning
Prompt Engineering
值不值得投
82综合评分
综合评分 82
Frontier Agentic AI role in pharma R&D with high social impact, flexible culture, and undisclosed but competitive compensation.
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
5.5万
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
89 个在招
市场机会
选择面较广
人工智能 · 约 1590 个在招
职位详情
关于这个职位
In this role, you will design, prototype, and deliver agentic AI solutions within the Advanced Data Science team to transform complex scientific and operational workflows in pharmaceutical R&D. You will apply large language models and emerging agentic AI methods, collaborating with scientists and engineers to translate use-case requirements into functioning AI applications.
最低要求
Hold a master’s degree or PhD in computer science, artificial intelligence, machine learning, data science, software engineering, or a related quantitative discipline.
Minimum of 3 years professional or research 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 the grounding of 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 Research & Development.
Work closely with scientists, business stakeholders, software engineers, machine learning experts, and the Lead Applied AI Engineer to translate use-case requirements into functioning AI applications.
Develop agents capable of reasoning over enterprise knowledge, interacting with scientific tools and data sources, executing multistep tasks, and collaborating effectively 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 that solutions include appropriate error handling, traceability, evaluation, and safeguards for their intended use.
Work in close partnership with software engineering and IT teams to integrate agentic workflows with approved enterprise platforms, data services, and applications.
Focus on the design and implementation of use-case-specific AI behaviour, complementing IT ownership of shared platforms, infrastructure, cybersecurity, and production operations.
Stay at the forefront of developments in foundation models, agentic systems, and AI engineering, rapidly assessing emerging approaches and translating those with practical value into solutions that improve scientific decision-making and productivity across R&D.
优先资格
Experience in pharmaceutical R&D, healthcare, life sciences, or another regulated environment is considered an advantage.
AI 洞察
优缺点分析
优点
- Work at the cutting edge of Agentic AI and LLMs in a highly impactful domain (pharma R&D).
- Opportunity to shape how AI transforms scientific discovery and directly impacts patient outcomes.
- Collaborate with diverse, global teams in a flexible and inclusive working culture.
- Navigating the complexities of regulated environments and ensuring AI reliability and safety in critical scientific workflows.
- Keeping up with the rapidly evolving landscape of foundation models and agentic systems to maintain a competitive edge.
- Experienced AI/ML engineers passionate about applying cutting-edge LLM and agentic technologies to solve complex scientific challenges in the healthcare sector.
缺点 / 挑战
暂无明显挑战项
角色解读
- Progress from Senior AI Engineer to Lead Applied AI Engineer or AI Architect within the data science organization.
- Transition into managing AI product lines or leading cross-functional data science teams within the pharmaceutical industry.
- Design and deliver agentic AI solutions using LLMs to transform scientific and operational workflows in pharma R&D.
- Develop AI agents capable of reasoning, tool use, and human-in-the-loop collaboration to execute multistep tasks.
- Evaluate and optimize agent configurations, prompts, and retrieval strategies for reliability, latency, and cost.
- Strong programming in Python and experience with LLMs, RAG, and agentic AI frameworks.
- Deep understanding of agentic concepts like reasoning, planning, memory, and workflow orchestration.
- Familiarity with APIs, cloud environments, and evaluating AI systems using quantitative metrics.
申请策略
- Tailor your resume to show how your AI solutions improved scientific decision-making or operational productivity.
- Be prepared to discuss specific use cases where you handled LLM limitations and implemented effective fallback mechanisms.
- Highlight hands-on experience with LLMs, RAG, and agentic AI frameworks, emphasizing modular and maintainable software design.
- Showcase projects where you moved from rapid prototyping to production readiness, including evaluation metrics and safeguards.
- Emphasize cross-functional collaboration with scientists, engineers, and IT teams to deliver impactful AI solutions.
- Deepen knowledge of context engineering, agent memory, and workflow orchestration to build more robust agentic systems.
- Gain familiarity with pharmaceutical R&D workflows or regulated environments to better understand the domain context.
面试指南
- Use the STAR method (Situation, Task, Action, Result) to structure your answers, focusing on technical trade-offs and business impact.
- Highlight the evaluation metrics you used, the safeguards you implemented, and how you iteratively improved the solution based on user feedback.
- Can you walk us through a complex agentic AI system you designed and deployed from prototyping to production?
- How do you evaluate the reliability, latency, and cost of LLM-based agents in a production environment?
- Describe your experience with RAG and context engineering, and how you ground model outputs in trusted sources.
- How do you handle the limitations and failure modes of LLMs in critical scientific workflows?
- Review the latest advancements in agentic AI, foundation models, and RAG techniques to demonstrate your technical depth.
- Prepare concrete examples of how you collaborated with cross-functional teams to translate complex requirements into functioning AI applications.
职位点评
薪资福利80
成长发展85
工作生活75
使命价值90
薪资福利
80较高
Competitive compensation for a senior AI role in Israel's tech and pharma market, backed by a well-established global healthcare company.
薪资信号未披露（AI估算：4千–7万/月）
成长发展
85较高
Strong focus on cutting-edge Agentic AI and LLMs, offering significant opportunities to work on frontier technologies and drive innovation in pharma R&D.
技术前沿前沿/新兴技术
技术栈Agentic AI、Large Language Models、RAG、Python、Prompt Engineering、Context Engineering、Workflow Orchestration
成长机会personal development、career advancement
业务类型ambiguous
工作生活
75中等
The role mentions a flexible working culture, which suggests a supportive work-life balance, though specific remote or hybrid details are not explicitly defined in the JD.
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
Directly contributes to developing medicines and intelligent devices to improve and prolong patients' lives, offering a strong sense of mission and social impact.
行业发展稳定成熟行业
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
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