Principal Architect

## **Career Category**Information Systems## ## **Job Description****Join Amgen’s Mission of Serving Patients**At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.**Principal Architecht****What you will do**Let’s do this. Let’s change the world. In this vital role you will play a pivotal role in building and scaling our machine learning models from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable ML pipelines. A background in data engineering, including experience with data pipelines and distributed data processing, is a strong plus.* Lead the **end-to-end design, development, and delivery** of machine learning and Generative AI (GenAI) solutions, leveraging **Databricks, Apache Spark, SQL, and Python** for scalable data processing, feature engineering, and model development from problem framing to production deployment and business impact realization.* Act as an **Architect for large-scale Data Engineering and ML/GenAI initiatives**, driving architecture decisions across **lakehouse platforms (Databricks), distributed compute (Spark), and cloud ecosystems (AWS/GCP/Azure)** to ensure scalability, reliability, and long-term maintainability.* Design and implement advanced **data pipelines and AI systems**, including **batch and streaming data processing (Spark), data modeling (SQL), and ML workflows (Python)**, along with multi-agent architectures, reasoning workflows, tool integration, and autonomous decision-making systems.* Build and optimize **robust data foundations** for AI by developing **high-quality, scalable ETL/ELT pipelines in Databricks**, ensuring data availability, consistency, and performance for downstream ML/GenAI use cases.* Define and institutionalize **evaluation, validation, and governance frameworks** for ML/GenAI systems, including model performance tracking, prompt evaluation, safety guardrails, hallucination mitigation, and compliance.* Partner directly with **business stakeholders and product leaders** to translate objectives into **data-driven AI/ML solutions**, ensuring measurable value through well-defined data pipelines, KPIs, and experimentation frameworks.* Establish and enforce best practices in **MLOps, LLMOps, DataOps, and DevOps**, including CI/CD pipelines, **Databricks workflows**, monitoring, observability, reproducibility, and cost optimization.* Architect and oversee **scalable cloud-based data and AI platforms**, integrating **Databricks Lakehouse, Spark processing layers, and cloud-native services** for unified analytics and AI workloads.* Drive experimentation strategy, including **A/B testing, prompt optimization, and data-driven iteration**, leveraging **SQL analytics and Python-based experimentation frameworks**.* Provide mentorship to L4 and L5 engineers in **data engineering (Spark, SQL, Databricks) and AI/ML development (Python, GenAI frameworks)**, including design reviews, code reviews, and career guidance.* Lead cross-functional collaboration across **data engineering, data science, platform engineering, and business teams** to deliver integrated, production-grade AI solutions.* Stay at the forefront of advancements in **data engineering (Spark ecosystem, lakehouse architectures)** and **Generative AI/agentic systems**, driving adoption of new technologies and best practices.**What we expect of you**We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a **Software Engineer** with these qualifications.**Basic Qualifications*** Doctorate degree and 2 years of experienceOR* Master’s degree and 4 years of experienceOR* Bachelor’s degree and 6 years of

experienceOR* Associate’s degree and 10 years of

experienceOR* High school diploma / GED and 12 years of experience**Preferred Qualifications*** Deep expertise in machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques).* Proven track record of **leading and delivering production-grade ML/GenAI systems end-to-end** with measurable business impact with strong experience in designing **scalable system architectures** for ML and GenAI, including distributed systems and high-throughput pipelines.* Expertise in **MLOps/LLMOps ecosystems** (MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes).* Strong system design, architecture, and problem-solving skills with the ability to operate independently and lead large initiatives.* Demonstrated proficiency in leveraging cloud platforms (AWS, Azure, GCP) for data engineering solutions. Strong understanding of cloud architecture principles and cost optimization strategies.* Proven ability to mentor and guide junior and mid-level engineers (L4/L5).**Good-to-Have Skills:*** **Cloud certifications** (AWS, Azure, or GCP) are a plus* Strong experience with **big data ecosystems**, including **Apache Spark, Hadoop**, and large-scale distributed data processing* Deep expertise in **data engineering**, including building and optimizing **scalable data pipelines and platforms** using **Databricks, Spark, SQL, and Python*** Advanced proficiency in **Python** and modern **ML/AI frameworks** (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, or similar)* Experience designing **robust evaluation and validation frameworks**, including automated evaluations, human-in-the-loop systems, safety testing, and monitoring* Extensive experience with **Retrieval-Augmented Generation (RAG)** architectures, **vector databases**, and knowledge-grounded AI systems* Strong understanding of **agentic AI frameworks**, including orchestration, planning, memory management, and tool integration* Solid foundation in **statistical modeling, experimentation design (A/B testing), and causal inference*** Experience with **NLP, semantic search, embeddings, and vector search systems*** Familiarity with **Responsible AI practices**, including fairness, explainability, governance, and regulatory compliance* Hands-on experience with **cloud-native AI/ML services** across **AWS, Azure, or GCP**, including cost and performance optimization* Experience with the **Databricks Lakehouse platform** for enterprise-scale **data engineering, ML, and GenAI workloads*** Exposure to advanced evaluation techniques such as **red-teaming, adversarial testing, and synthetic data generation*** Strong experience in **data modeling and performance tuning** for both **OLAP and OLTP systems*** Hands-on experience with **workflow orchestration tools** such as **Apache Airflow**, and distributed processing frameworks like **Apache Spark**## **What you can expect of us**As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.The expected
#J-18808-Ljbffr