Principal Architect

Amgen

. Lead end-to-end design, development, and delivery of machine learning and Generative AI solutions .

Posted 11/2/2026full-timeRemote • United StatesLead💰 $144,424 - $195,397 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and delivering machine learning and Generative AI solutions, with a strong focus on MLOps, cloud architecture, and scalable data pipelines. Proficient in collaborating with cross-functional teams to translate business objectives into effective AI/ML strategies.

Highest-signal resume keywords

Machine Learning ExpertiseGenerative AI SolutionsMLOps/LLMOps EcosystemsCloud Architecture ProficiencyDatabricks and Spark Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningGenerative AIData Pipeline DevelopmentStatistical ModelingPython ProgrammingSQLApache SparkTensorFlowPyTorch

Soft Skills

MentoringCollaborationCommunication

Tools & Technologies

DatabricksAWSAzureGCPMLflowKubeflowAirflowDockerKubernetesCI/CD

Certifications & Qualifications

Cloud Certifications

Industry Keywords

MLOpsDataOpsResponsible AIEvaluation FrameworksAdversarial TestingOLAPOLTPVector DatabasesNLPSemantic Search

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudDockerETLGoogle Cloud PlatformHadoopKubernetesPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Lead end-to-end design, development, and delivery of machine learning and Generative AI solutions
Drive architecture decisions across Databricks lakehouse platforms, Spark, and AWS/GCP/Azure ecosystems
Design and implement batch and streaming data pipelines, data models, ML workflows, multi-agent architectures, and autonomous decision-making systems
Build and optimize scalable ETL/ELT pipelines in Databricks
Define evaluation, validation, governance, safety, and compliance frameworks for ML/GenAI systems
Partner with business stakeholders and product leaders to translate objectives into measurable AI/ML solutions
Establish best practices in MLOps, LLMOps, DataOps, and DevOps, including CI/CD, monitoring, observability, reproducibility, and cost optimization
Architect and oversee scalable cloud-based data and AI platforms
Drive experimentation strategy, including A/B testing and prompt optimization
Mentor L4 and L5 engineers through design reviews, code reviews, and career guidance
Lead collaboration across data engineering, data science, platform engineering, and business teams
Drive adoption of advancements in data engineering and Generative AI/agentic systems

Requirements

What you’ll need

Doctorate degree and 2 years of experience, OR Master’s degree and 4 years of experience, OR Bachelor’s degree and 6 years of experience, OR Associate’s degree and 10 years of experience, OR high school diploma/GED and 12 years of experience
Deep expertise in machine learning, deep learning, and Generative AI
Experience delivering production-grade ML/GenAI systems end-to-end
Expertise in MLOps/LLMOps ecosystems, including MLflow, Kubeflow, Airflow, CI/CD, Docker, and Kubernetes
Proficiency with AWS, Azure, or GCP and cloud architecture principles
Experience with big data ecosystems, Apache Spark, Hadoop, and distributed data processing
Expertise building scalable data pipelines and platforms with Databricks, Spark, SQL, and Python
Proficiency in Python and modern ML/AI frameworks such as PyTorch, TensorFlow, Hugging Face, and LangChain
Experience designing evaluation and validation frameworks
Experience with RAG architectures, vector databases, and knowledge-grounded AI systems
Understanding of agentic AI frameworks, orchestration, planning, memory management, and tool integration
Foundation in statistical modeling, A/B testing, and causal inference
Experience with NLP, semantic search, embeddings, and vector search systems
Familiarity with Responsible AI practices, fairness, explainability, governance, and regulatory compliance
Experience with cloud-native AI/ML services and cost/performance optimization
Experience with Databricks Lakehouse
Experience with red-teaming, adversarial testing, and synthetic data generation
Experience with OLAP and OLTP data modeling and performance tuning
Cloud certifications are a plus

Benefits

Comp & perks

Retirement and Savings Plan with generous company contributions
Group medical, dental and vision coverage
Life and disability insurance
Flexible spending accounts
Discretionary annual bonus program
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible
Career development opportunities
Work/life balance support