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Amgen

Principal Architect

Washington D.C., DC, United States

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Job details

Posting language: English
Posted: Apr 27, 2026

Original 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 Architect

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 Principal Architect with these qualifications.

Basic Qualifications

Doctorate degree and 2 years of experience

Master’s degree and 4 years of experience

Bachelor’s degree and 6 years of experience

Associate’s degree and 10 years of experience

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 annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.

In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:

A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies.

Apply now and make a lasting impact with the Amgen team.

careers.amgen.com

In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.

Application deadline

Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.

Sponsorship

Sponsorship for this role is not guaranteed.

As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.

Amgen is an Equal Opportunity employer. We make all employment decisions without regard to race, color, religion, sex or any other characteristic protected by applicable law.

We provide reasonable accommodations to applicants and employees with disabilities, consistent with legal requirements. Please contact us to request accommodation.

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More about this job

Skills

Machine learning
ML
Data engineering
Process data
Databricks
Apache Spark
SQL
Python
AWS
Google Cloud
Microsoft Azure
Data modelling
Decision making
Regulatory compliance
DevOps
CI/CD
Provide mentorship
Collaboration
Deep learning
Tuning techniques
Mlflow
Kubeflow
Apache Airflow
Docker
Kubernetes
Problem solving
Apache Hadoop
PyTorch
TensorFlow
Hugging Face
LangChain
Natural language processing
Workflow
Compensation and benefits
Sales

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