Sr Assoc Digital Intelligence

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. 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.

The role

Join a hands-on team building the next generation of AI-enabled Procurement. As Senior Associate, Digital Intelligence & Enablement, you'll combine data engineering and Generative AI skills to turn use cases into reliable products. You'll help stand up pilots, wire the data, build retrieval/RAG and prompt flows, and move the winners to production - improving speed, cost, compliance, and supplier experience across Global Procurement.

What you'll do

• Build the data backbone: develop and maintain pipelines from ERP/P2P, CLM, supplier, AP, and external sources into governed, analytics/AI-ready datasets (gold tables, lineage, quality checks).
• Implement GenAI capabilities: stand up retrieval-augmented generation (embeddings, vector stores), prompts/chains, and lightweight services/APIs for RFx, contract intelligence, guided intake, and risk sensing.
• Ship pilots, measure value: contribute to 8–12 week pilots with clear baselines; instrument telemetry and dashboards; help decide continue/pivot/scale.
• Harden for production: package code, automate CI/CD, add evaluation and observability (quality, drift, latency, cost), and support incident triage with platform teams.
• Partner & document: collaborate with category teams, AI/ML platform, IT Architecture, Security/Privacy, and vendors; produce clear runbooks and user guides.

Minimum qualifications

• 3+ years in data engineering/analytics/ML engineering delivering production-grade pipelines and services.
• Strong SQL and Python; experience with ETL/ELT tools (e.g., dbt, Airflow) and cloud data platforms (e.g., Snowflake/BigQuery/Azure Synapse/Databricks).
• Practical exposure to GenAI/LLMs: prompt design, RAG patterns, embeddings, vector databases, and LLM APIs.
• Familiarity with APIs/integration, version control, testing, and CI/CD.
• Clear communicator who collaborates well across business, data, and engineering teams.

Preferred qualifications

• Experience with S2P/CLM/AP data (e.g., SAP/Ariba) or supplier-risk/market data.
• Knowledge of LLM orchestration frameworks (e.g., LangChain, LlamaIndex) and vector stores (e.g., FAISS, Milvus, Pinecone).
• Exposure to MLOps/LLMOps (evaluation frameworks, prompt registries/guardrails, tracing/observability).
• Cloud experience (Azure/AWS/GCP), containers (Docker), and monitoring (e.g., MLflow, Prometheus/Grafana).
• BI skills (e.g., Power BI) and data quality tooling.

What success looks like (first 12 months)

• Delivered 2+ pilots to production with documented KPI improvements (cycle time, automation %, accuracy).
• Established trusted data assets (gold tables, lineage, tests) for at least two priority domains.
• Operationalized at least one RAG application with evaluation and cost/latency observability.
• Positive feedback from users and partners; clear, reusable runbooks and patterns.

Why this role

• Impact: Build real AI products used across Procurement.
• Growth: Stretch across data engineering, GenAI, and platform practices.
• Collaboration: Work with experts across AI/ML, architecture, security, and leading vendors.

How to apply: Send your resume or profile. If available, include a brief note on a data/GenAI project you built and the outcome you're most proud of.