Overview

Amgen harnesses the best of biology and technology to fight the world's toughest diseases, and make people's lives easier, fuller and longer.

Responsibilities

• Define and drive the technical strategy for enterprise forecasting and AI decision systems, aligning architecture, reusable platforms, and delivery roadmaps to Amgen's planning, supply, commercial, manufacturing, operations, and patient-focused priorities.
• Partner with data scientists, product and program leaders, operations, commercial, manufacturing, supply chain, finance, and other business stakeholders to translate ambiguous requirements into shipped software and measurable business outcomes.
• Architect, build, and scale production ML, LLM, and agentic AI systems that combine forecasting, predictive analytics, simulation, optimization, and autonomous or semi-autonomous workflow automation.
• Productionize advanced statistical, Bayesian, deep learning, and machine learning models, including training, validation, inference, serving, evaluation, lifecycle management, and governed deployment.
• Lead development of AI agent components that automate complex forecasting and operational workflows across multiple systems, decision points, datasets, and user groups while preserving appropriate human-in-the-loop review and escalation patterns.
• Design secure integrations across enterprise APIs, databases, analytics platforms, workflow systems, cloud services, and AI orchestration patterns to enable multi-system decision support and scalable automation.
• Establish robust MLOps and AI engineering capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, observability, drift detection, service-level reliability, rollback strategies, and operational runbooks.
• Implement guardrails, model and agent evaluation frameworks, auditability, explainability, responsible AI controls, and human-in-the-loop operating models for production AI systems in high-impact and regulated business contexts.
• Research and evaluate state-of-the-art open-source, vendor, and internal tools related to forecasting, LLMs, AI agents, MLOps, model optimization, model serving, and scalable AI infrastructure for potential application to Amgen business problems.
• Provide principal-level technical mentorship, design review leadership, and engineering standard-setting across teams, promoting code quality, documentation, reproducibility, testing, security, privacy, maintainability, and operational excellence.
• Influence AI & Data Science technical and product roadmaps by identifying reusable patterns, platform opportunities, and enterprise-wide automation opportunities that accelerate Amgen's ability to serve patients.

Qualifications

• Degree and 12+ years of experience in machine learning engineering, software engineering, data science engineering, or a related quantitative discipline.
• 10+ years of professional experience building, deploying, and operating production ML, AI, data, or software systems, including significant experience as a technical lead on complex, cross-functional initiatives.
• Demonstrated track record of designing or architecting new and existing systems with emphasis on reliability, scale, security, maintainability, and operational excellence.
• Deep hands-on experience with the full ML engineering lifecycle, including data pipelines, feature engineering, experimentation, model training, model integration, testing, deployment, monitoring, evaluation, observability, and continuous improvement.
• Strong experience deploying forecasting, probabilistic, Bayesian, predictive, NLP, deep learning, or LLM-based systems in production environments.
• Strong object-oriented programming skills in Python and SQL, with experience using modern ML and software development frameworks such as scikit-learn, PyTorch, TensorFlow/JAX, Spark, Ray, MLflow, Airflow/Prefect/Dagster, FastAPI, or equivalent technologies.
• Strong software engineering fundamentals, including system design, architecture trade-off analysis, testing strategies, code reviews, source control, build and release processes, performance optimization, and maintainability.
• Demonstrated ability to communicate technical strategy, system tradeoffs, and delivery risks to technical and non-technical stakeholders, including senior leaders, product/program owners, scientists, and business partners.
• Demonstrated ability to lead through ambiguity, define technical direction, mentor others, and deliver measurable business value in a matrixed enterprise environment.
• Knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, coverage, patient access, channel dynamics, and product lifecycle considerations.
• Publications, patents, open-source contributions, conference presentations, or other evidence of thought leadership in machine learning engineering, forecasting, LLMs, AI systems, MLOps, or decision intelligence.