Principal

Job Description

Let s do this. Let s change the world. We are seeking a Principal AI/ML Engineer (Agents and Agentic Design) to drive innovation in AI/ML and agent-based system design at Amgen. This role is integral to advancing the organizations AI strategy by developing intelligent, scalable, and secure multi-agent systems. The Principal AI/ML Engineer will be responsible for leading technical innovation, mentoring AI/ML teams, and integrating agent-based solutions into enterprise platforms. The key aspects of this role involve: Prior hands-on experience designing and implementing sophisticated AI/ML systems with multiple interacting agents. Leadership experience in guiding AI/ML teams and setting strategic direction for agent-based frameworks. Delivering AI/ML solutions that enhance efficiency, scalability, and business value across enterprise-level platforms. Roles & Responsibilities: AI/ML Thought Leadership & Mentorship: Mentor and provide thought leadership for AI/ML engineers and data scientists, enabling continuous technical growth in agentic design, reinforcement learning, and AI standard methodologies. Agent-Based System Development: Design, implement, and optimize multi-agent AI/ML systems that enhance automation, decision-making, and system efficiency. Strategic Framework Development: Define and set the direction for AI/ML frameworks that enable scalable and robust agentic solutions. Enterprise-Level Integration: Work closely with multi-functional teams to integrate agent-based solutions into Amgen s enterprise platforms, aligning AI strategies with business objectives. Technical Innovation & Research: Stay at the forefront of AI/ML advancements, exploring and implementing new methodologies such as reinforcement learning, active learning, and RLHF to drive continuous improvement. Cross-Functional Collaboration: Partner with leadership, product teams, and other collaborators to identify AI opportunities, streamline implementations, and align AI/ML strategies with company goals. Quality & Performance Assurance: Ensure that AI solutions meet enterprise-scale performance, security, and reliability standards, while maintaining detailed documentation for long-term scalability. What we expect of you We are all different, yet we all use our unique contributions to serve patients. Basic Qualifications: Master s degree and 8 to 10 years of AI, machine learning, and/or data science experience OR Bachelor s degree and 10 to 14years of AI, machine learning, and/or data science experience OR Diploma and 14 to 18 years ofAI, machine learning, and/or data science experience Proven expertise in designing and implementing AI/ML systems with multiple interacting agents. Demonstrated experience in reinforcement learning, active learning, and RLHF methodologies. Strong background in authoring and managing highly complex technical documentation at an enterprise level. Extensive experience in AI/ML system design, with a focus on multi-agent interactions and agentic design. Deep familiarity with agent-based modeling tools and frameworks. Proficiency in reinforcement learning, active learning, and RLHF methodologies. Strong background in developing scalable AI architectures, ensuring robustness, efficiency, and security. Experience with enterprise-scale AI/ML deployments and integrating agent-based solutions into business processes. Knowledge of cloud-based AI/ML frameworks and tools for large-scale model training and deployment. Preferred Qualifications: Familiarity with data security and privacy regulations as they pertain to AI/ML applications. Experience in writing and managing complex technical documentation at an enterprise level. Strong project management skills with a track record of delivering AI-driven initiatives on time and within scope. Soft Skills: Leadership & Vision: Ability to inspire and guide teams toward achieving AI/ML innovation and excellence. Strategic Thinking: Capable of developing and implementing long-term AI strategies that align with business needs. Collaboration: Proven experience working across different teams and subject areas to drive AI/ML integration. Communication: Ability to convey complex AI/ML concepts to both technical and non-technical collaborators. Adaptability: A forward-thinking approach that embraces emerging AI technologies and evolving industry trends.