Sr Assoc Digital Intelligence & Enablement

The role

Join a hands-on team building the next generation of AI-enabled Procurement. As Senior Associate, Digital Intelligence & Enablement, youll combine data engineering and Generative AI skills to turn use cases into reliable products. Youll 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 youll 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 812 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.