Senior Applied AI & Intelligent Automation Architect: Amgen

About the Role (Applied AI & Agentic Automation)
This is a senior position within Amgen's AI & Data Science (AI&D) organization. The role is for a highly skilled, innovative Specialist IS Architect to design, develop, and deploy scalable AI/ML systems with a strong emphasis on agentic automation, Generative AI (GenAI), and MLOps guidelines. This hands-on architect will work on next-generation capabilities using COTS products, open-source software, and cloud computing services, bridging the maturity spectrum from Robotic Process Automation (RPA) to complex Agentic systems.

Key Responsibilities
Full-Stack AI: Build and maintain full-stack AI applications, ensuring performance, scalability, and cost efficiency.

Agentic AI: Design and implement agentic AI systems that autonomously perform sophisticated tasks, demonstrating LLMs, multi-agent orchestration, and tool integration.

Automation Workflow: Design, develop, and deploy automation workflows using platforms such as UiPath and Power Automate.

Integration: Securely integrate all solutions with enterprise systems (e.g., SAP, Salesforce, ServiceNow) via robust APIs and least-privilege access controls.

Standards & MLOps: Contribute to the development of procedures and standards to continually improve delivery of intelligent automation and applied AI (GenAI, agentic, and ML) projects, and conduct peer code reviews.

Technology Leadership: Continuously learn and stay informed on emerging automation technologies and trends (e.g., LangChain, N8N, CrewAI).

Qualifications and Experience
Basic Qualifications
A Bachelor’s degree and 4 years of experience in computer science, data science/engineering, information technology (IT), or a related field. Equivalent combinations of advanced education and experience are also accepted (e.g., Master’s degree and 2 years).

Must-Have Skills (Preferred)
Agentic AI Frameworks: Experience with agentic AI frameworks across the no-code, low-code, and pro-code spectrum and with multi-agent orchestration. Strong working knowledge of agent-based automation such as LangChain, LangGraph, and UiPath.

ML Mastery: Command of machine learning algorithms (regression, tree-based, deep learning, and transformers) and modern LLM/RAG techniques.

Core Tech Stack: Proficiency in Python, ML frameworks (TensorFlow, PyTorch), and cloud platforms (AWS or Azure), with hands-on experience using MLOps tools and managing the AI model lifecycle.

GenAI Architecture: Strong understanding of LLM-based architecture, prompt engineering, and fine-tuning techniques.

Automation Platform: Proficiency in designing, developing, and deploying automation workflows using platforms such as UiPath and Power Automate.

Good-to-Have Skills
Familiarity with vector databases and Retrieval-Augmented Generation (RAG).

Knowledge of architecting and deploying end-to-end machine learning pipelines using modern MLOps stacks such as Kubeflow, Amazon SageMaker.

Experience in the pharma or biopharma industry (or healthcare).

Knowledge of modern SAP platforms (preferably S/4HANA).

Familiarity with API integration technologies (ESB, REST, SOAP).