Service Supply Chain AI Engineer

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must only include sections that have real specific content. We must not repeat job title as heading. Must not include Job ID etc. Must preserve all substantive info, remove boilerplate like EEO, legal, etc. Must not invent or summarize away detail. Must not include sections with filler like "commensurate with experience". Must only include sections where posting has real specific content. Let's parse posting. Content: Intro paragraph: "To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale—faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology." Then "Group/Division 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." Then "What You'll Do In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise." Then bullet list of responsibilities. Then "What Success Looks Like (examples Of Outcomes)" with bullet list. Then "Minimum Qualifications Skills & Experience Needed Required Skills (must-have)" then list of required skills. Then "Preferred Skills (nice-to-have)" list. Then "Education & Qualifications Minimum / typical qualification guidelines ..." Then "About KLA" paragraph. Then "Total Rewards Base Pay Range: $90,400.00 - $132,600.00 Annually Primary Location: USA-MI-Ann Arbor-KLA KLA’s total rewards package for employees may also include participation in performance incentive programs and eligibility for additional benefits including but not limited to: medical, dental, vision, life, and other voluntary benefits, 401(K) including company matching, employee stock purchase program (ESPP), student debt assistance, tuition reimbursement program, development and career growth opportunities and programs, financial planning benefits, wellness benefits including an employee assistance program (EAP), paid time off and paid company holidays, and family care and bonding leave. Interns are eligible for some of the benefits listed. Our pay ranges are determined by role, level, and location. The range displayed reflects the pay for this position in the primary location identified in this posting. Actual pay depends on several factors, including state minimum pay wage rates, location, job-related skills, experience, and relevant education level or training. We are committed to complying with all applicable federal and state minimum wage requirements where applicable. If applicable, your recruiter can share more about the specific pay range for your preferred location during the hiring process." Then "Use of AI Statement" etc. That's boilerplate? It's about interview policy. Might be considered boilerplate? The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "Use of AI Statement" might be considered legal/boilerplate? It's about interview process. Probably should remove. Also "Equal Opportunity Statement" definitely remove. Thus we need sections: About the role? Could be intro paragraph. Could be "About the role" heading with that intro paragraph. Or we could combine with "Group/Division". The instruction: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can choose headings that match content. We have: - About the role: could include the introductory paragraph and group/division description. - Responsibilities: the "What You'll Do" bullet list. - Requirements: maybe "Minimum Qualifications Skills & Experience Needed Required Skills (must-have)" and "Preferred Skills (nice-to-have)"? But they said separate sections: Requirements, Qualifications, Skills. Might be overlapping. We need to decide. We have "Minimum Qualifications Skills & Experience Needed Required Skills (must-have)" which is basically required skills. Then "Preferred Skills (nice-to-have)". Then "Education & Qualifications Minimum / typical qualification guidelines". So we could have: - Requirements: maybe combine required and preferred skills? But they said prefer few substantive sections over many thin ones. Could combine Required and Preferred into a Skills section? But they also have Skills as a possible heading. Let's think. We could have sections:

About the role

(intro + group/division)

Responsibilities

(What You'll Do bullet list)

Skills

(Required Skills and Preferred Skills bullet lists)

Qualifications

(Education & Qualifications)

Benefits

(Total Rewards benefits list)

Pay

(Base Pay Range) We should not include Schedule (no info). Also not include Requirements separate if we combined into Skills. But we could also have Requirements as required skills and Qualifications as education. But they said we can use those headings. Let's see if we need both Requirements and Qualifications. The posting has "Minimum Qualifications Skills & Experience Needed Required Skills (must-have)" and "Preferred Skills (nice-to-have)" and "Education & Qualifications Minimum / typical qualification guidelines". So we could map: - Requirements: Required Skills (must-have) - Qualifications: Education & Qualifications - Skills: Preferred Skills? Or combine both required and preferred into Skills? But they'd be thin. Better: Use Requirements for required skills, Qualifications for education, Skills for preferred skills. That yields three sections. But we also have Benefits and Pay. Thus sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay. Check each for real specific content. About the role: includes intro paragraph and group/division description. That's substantive. Responsibilities: bullet list of duties (What You'll Do). Good. Requirements: list of required skills (AI/ML & analytics, Graph theory & graph data, Software & data engineering, Collaboration). That's specific. Qualifications: Education & Qualifications guidelines (MS/PhD etc). Specific. Skills: Preferred Skills list (supply chain planning experience, probabilistic forecasting, knowledge graphs, LLM integration, MLOps). Specific. Benefits: list of benefits (medical, dental, vision, life, voluntary benefits, 401K, ESPP, student debt assistance, tuition reimbursement, development opportunities, financial planning, wellness benefits including EAP, paid time off, paid company holidays, family care and bonding leave). Specific. Pay: Base Pay Range: $90,400.00 - $132,600.00 Annually. Also mention primary location. That's specific. We should not include location as separate heading; we can include location in Pay paragraph or maybe in About the role? But we can include location in Pay section as part of description. We must not include boilerplate like Use of AI Statement, Equal Opportunity Statement. Also remove any duplicated lines. We must preserve all substantive information; do not invent or summarize away detail. So we need to keep the exact bullet points? We can keep them as

• . Must not lose any detail. Let's extract the exact text. Intro paragraph: "To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale—faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology." Group/Division paragraph: "Group/Division 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." We can combine these two paragraphs under About the role. Now Responsibilities: "What You'll Do In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise." Then bullet list: - Build AI tools that planners actually use - 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 - Design and implement graph databases for AI use cases - 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. - Apply ML / AI to spares planning outcomes - 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. - Productionize: reliability, governance, and MLOps - 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). We need to preserve all bullet points and sub-bullets. Use
  • for any list of duties/requirements/benefits. For sub-bullets, we can nest
    • inside. Now Requirements: "Minimum Qualifications Skills & Experience Needed Required Skills (must-have)" then bullet list: - AI / ML & analytics - 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. - Graph theory & graph data - 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). - Software & data engineering - 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. - Collaboration - Proven ability to work with non-technical stakeholders, convert ambiguous business needs into effective tools, and drive adoption/change management. Now Qualifications: "Education & Qualifications Minimum / typical qualification guidelines MS or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, Operations Research, Industrial Engineering, or a related quantitative field; ORMS + 3+ years relevant experience; ORBS + 5+ years relevant experience in software engineering / ML engineering / data engineering with demonstrated delivery of