Lead AI Engineer, Knowledge & Semantic Systems

The user wants me to convert this job posting into clean semantic HTML. Let me analyze the content and structure it according to the rules. First, I need to identify the sections: 1. Company overview (About the company) - but the rules say to use h3 for section headings like "About the role", "Responsibilities", etc. The company description is more of an intro. 2. Overview / About the role 3. Key Responsibilities 4. Technology Environment 5. Qualifications (Required) 6. Preferred (Preferred Experience) 7. Note about hybrid work schedule 8. Pay information (salary range) 9. Benefits information The rules say: use h3 for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use ul/li for lists, p for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Preserve ALL substantive information. Remove boilerplate (cookie/navigation/legal/EEO/scam-warning). Remove duplicated lines and application instructions. Important: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text. Never create Pay/Schedule/Benefits sections unless the posting actually states them. The posting has: - Company description (prose) - Overview (About the role) - Key Responsibilities (list) - Technology Environment (list) - Qualifications Required (list) - Preferred Experience (list) - Note about hybrid schedule (Schedule) - Pay range (Pay) - Benefits (Benefits) - Some boilerplate at the end (EEO, scam warning, visa sponsorship) - should remove. I should structure as: - h3 About the role (or Overview) - h3 Responsibilities - h3 Technology Environment - h3 Qualifications - h3 Preferred Qualifications (or Preferred Experience) - h3 Schedule (since it mentions hybrid work schedule) - h3 Pay (since it gives salary range) - h3 Benefits (since it lists benefits) But note: the rules say "Prefer a few substantive sections over many thin ones." So I should combine where appropriate. Also, the company description at the top: should I include it? It's substantive. Could be under "About the Company" but the rules don't list that as a standard heading. The example headings are "About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule". The company description is not "About the role". However, it's part of the posting. I think I should include it as a section, maybe "About the Company" or "Company Overview". But the rules say use h3 for section headings (e.g. About the role, Responsibilities...). It doesn't forbid other headings. But to be safe, I'll include it as a section with h3 "About the Company" or "Company Overview". However, the user said "Do NOT repeat the job title as a heading (the page already shows it)". The job title is "Lead AI Engineer, Knowledge & Semantic Systems". That's not repeated. I'll include the company description as a section. Now, let's parse the content. Company description: first paragraph up to "Overview". That's about Bausch + Lomb. Then "Overview" section: "The Lead AI Engineer, Knowledge & Semantic Systems will build and maintain..." That's about the role. Then "Key Responsibilities": a list of bullet points (each starting with a verb). They are separated by line breaks. I'll make a ul with li for each. Then "Technology Environment": list of technologies. Then "Qualifications Required:" with bullet points. Then "Preferred Experience" with bullet points. Then "Note: This role is eligible for our hybrid work schedule..." That's schedule. Then "For U.S. locations that require disclosure of compensation, the starting pay for this role is between $150,000.00 and $200,000.00..." That's pay. Then "U.S. based employees may be eligible for short-term and/or long-term incentives. They may also be eligible to participate in medical, dental, vision insurance, disability and life insurance, a 401(k) plan and company match, a tuition reimbursement program (select degrees), company holidays, and well-being benefits, among others. U.S. based employees are also eligible to receive sick time, floating holidays and paid vacation." That's benefits. Then boilerplate: "Job Applicants should be aware of job offer scams..." and "Our US Benefit Programs Employee Benefits: Bausch + Lomb Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time." Remove. Also the EEO statement: "All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status." That's boilerplate, remove. Now, I need to output ONLY the HTML fragment, no html/body, no markdown code fences. Let's write the HTML. I'll use h3 for each section. For lists, use ul/li. For prose, use p. I'll combine the company description and overview? They are separate. I'll keep separate sections: "About the Company" and "About the Role". But the rules say "About the role" is an example heading. I'll use "About the Role" for the overview. Let's structure:

About the Company

Bausch + Lomb (NYSE/TSX: BLCO) is a leading global eye health company dedicated to protecting and enhancing the gift of sight for millions of people around the world—from the moment of birth through every phase of life. Our mission is simple, yet powerful: helping you see better, to live better. Our comprehensive portfolio of over 400 products is fully integrated and built to serve our customers across the full spectrum of their eye health needs throughout their lives. Our iconic brand is built on the deep trust and loyalty of our customers established over our 170-year history. We have a significant global research, development, manufacturing and commercial footprint of approximately 13,000 employees and a presence in approximately 100 countries, extending our reach to billions of potential customers across the globe. We have long been associated with many of the most significant advances in eye health, and we believe we are well positioned to continue leading the advancement of eye health in the future.

About the Role

The Lead AI Engineer, Knowledge & Semantic Systems will build and maintain the foundational data, knowledge, and retrieval systems that power enterprise AI solutions. This highly technical individual contributor role focuses on creating trusted semantic models, knowledge graphs, metadata frameworks, and retrieval capabilities that enable accurate, scalable, and compliant AI-driven insights across the organization. You will help shape the data and knowledge foundation supporting enterprise AI capabilities while collaborating across data engineering, AI, analytics, and business teams to deliver innovative solutions that improve decision-making and business performance.

Responsibilities

• Design, build, and maintain the governed semantic layer for commercial; canonical metric definitions, dimensions, hierarchies, joins, grain, and row- and role-level access policy so that agents and BI consume one certified definition rather than reconstructing business logic per query.
• Codify commercial metric definitions including TRx, NRx, NBRx, NC TRx, market share and market basket, decile and segment assignment, and projected versus captured volume, capturing source system, calculation, refresh cadence, and known limitations for each, and arbitrate conflicting definitions across brand, field, market access, and finance.
• Build constraints that prevent semantically meaningless queries; combinations that are technically joinable but produce misleading results, such as blending projected and actual volumes, mixing incompatible grains, or computing share across non-comparable sources and enforce them in the layer rather than documenting them and hoping.
• Design and extend the commercial ontology and knowledge graph, covering ECP, account, affiliation and parent-child hierarchy, payer and plan, formulary status, product, territory and alignment, content asset, and engagement, modeling the relationships agents traverse rather than leaving them to be inferred.
• Work with enterprise team and apply entity resolution to Semantic definitions across sources used for Agents, reconciling prescriber and account identity across prescription, claims, specialty pharmacy, and CRM feeds, maintaining group practice and affiliation structures, and handling the reality that an eye care professional may be prescriber, account contact, and point of dispense simultaneously.
• Maintain taxonomies, controlled vocabularies and synonym and alias mappings so that varied business phrasings resolve onto the same governed concept, and so agents disambiguate consistently across users and brands.
• Encode temporal validity for territory realignments, formulary changes, affiliation changes, and segment refreshes, so historical questions return historically correct answers rather than today’s structure applied to last year’s data.
• Own the retrieval stack end to end; chunking strategy, embedding model selection and evaluation, index configuration and tuning.
• Treat retrieval quality as a measured discipline, building labeled query sets and tracking recall and precision at k, groundedness, and citation accuracy, with regression testing so that an embedding, chunking, or corpus change cannot silently degrade every downstream agent.
• Manage reindexing, embedding drift, model migration, and the cost and performance of vector infrastructure at production scale.
• Expose semantic, graph, and retrieval capability to agents through well-specified interfaces; Model Context Protocol (MCP) servers and resources, tool contracts, and structured schemas including the disambiguation hints, synonym maps, and worked examples that make correct agent behavior the default.
• Partner with Applied AI Agents engineers as their primary upstream dependency, operating on a shared sprint cadence and being measured in part on the answer accuracy and delivery velocity of the agents that consume your work.
• Diagnose grounding failures: when an agent returns a wrong or unsupported answer, determine whether the cause is definition, entity resolution, retrieval, or model reasoning, and remediate what belongs to this layer.
• Operate a federated contribution model, accepting proposed metrics, entities, and relationships from analysts and agent engineers, reviewing them for consistency, and curating the canonical model so it extends without fragmenting.
• Partner with Data Engineering, IT, Privacy, and Compliance to enforce access policy, PDRP and HIPAA minimum-necessary handling, and audit trails at the definition and retrieval layer rather than at the application layer.
• Document and socialize the model and run enablement so analysts and engineers adopt governed definitions instead of rebuilding them locally.

Technology Environment

• Python & SQL
• Azure/AWS
• Databricks & Snowflake
• Knowledge Graphs & Semantic Modeling
• Vector Databases & Retrieval Systems

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Science, Machine Learning, or a related field.
• 5+ years of experience in data engineering, analytics, semantic modeling, or knowledge engineering.
• Expertise in SQL, dimensional data modeling, and business metric governance.
• Hands-on experience with semantic layer tools (e.g., dbt Semantic Layer, MetricFlow, Cube, LookML, AtScale).
• Experience designing and managing knowledge graphs, ontologies, and graph databases (e.g., Neo4j).
• Experience with entity resolution, identity management, and master data concepts.
• Experience with vector databases, retrieval systems, and search optimization.
• Understanding of LLM-enabled solutions, including RAG, tool calling, structured outputs, and MCP.
• Strong Python development skills and