Service Supply Chain AI Engineer

KLA•Ann Arbor, MI

7d•Onsite

About The Position

In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise. The KLA Services team consists of Service Sales, Marketing, Spares Supply Chain Management, Field Operations, Engineering, Product Training, Digital Solutions and Analytics, and Technical Product Support. Our services organization maximizes the value of our customers' KLA assets – with highly trained service and product support engineers providing installation services and 24/7 technical support and parts delivery through our extensive supply chain network.

Requirements

Strong Python skills for data and ML development (pandas/numpy, ML libraries, model evaluation).
Experience developing customer demand prediction models, or other operational decision problems.
Solid foundation in graph theory concepts (graph modeling, connectivity, centrality, communities, bipartite/multipartite graphs, temporal graphs).
Hands-on experience building with a graph database (e.g., Neo4j or similar): schema design, query patterns, performance considerations.
Familiarity with graph embeddings and/or graph ML concepts (node/edge embeddings, message passing, link prediction, similarity).
Strong SQL and data modeling; ability to build reliable pipelines across large enterprise datasets.
Experience building production services or internal tools (APIs, web apps, dashboards) with a focus on usability and maintainability.
Proven ability to work with non-technical stakeholders, convert ambiguous business needs into effective tools, and drive adoption/change management.

Nice To Haves

Supply chain planning experience (service parts, inventory optimization, safety stock, service-level tradeoffs, replenishment/network concepts).
Experience with probabilistic forecasting approaches and intermittent-demand methods.
Knowledge graphs / ontology design, entity resolution, and “semantic” modeling patterns.
Experience integrating LLMs with structured data (RAG patterns, tool calling, natural-language-to-query workflows) where governance is required.
MLOps and platform experience: model tracking, CI/CD, monitoring, containers, scalable compute.

Responsibilities

Develop and maintain internal tools (apps, dashboards, workflows) that operationalize advanced analytics for spares planning decision-making.
Translate planning problems into well-scoped product requirements: user journeys, success metrics, data needs, and rollout plans.
Create “self-serve” tools that reduce manual effort and scales insights across the organization.
Define the graph data model (nodes/edges, ontology/taxonomy, temporal relationships, metadata) to represent spares demand, parts, tools, configurations, sites, and operational signals.
Build ingestion pipelines and data quality checks to keep the graph accurate, explainable, and trusted.
Enable AI and analytics on top of the graph: graph traversals, similarity search, embeddings, and graph ML patterns that support decision tools.
Develop predictive models for demand forecasting (including intermittent/long-tail behavior), demand drivers, and related planning signals.
Support inventory planning improvements (e.g., safety stock, multi-echelon thinking, service-level tradeoffs) by connecting model outputs to actionable recommendations.
Partner with SMEs to validate model behavior, define guardrails, and ensure outputs are usable and explainable in operational settings.
Implement testing, monitoring, documentation, versioning, and performance practices so tools are robust and maintainable.
Establish repeatable deployment patterns (dev/test/prod), model monitoring, and data lineage appropriate for enterprise planning environments.
Create clear documentation and enablement materials so tools can be adopted broadly (not just by technical users).