Role: Lucidworks Fusion Search Platform Engineer
Location: Remote
Pay Rate: $85/Hr on W2 all incl
Scope of Position
Lead the design, build, tuning, and operation of the search layer for Corning.com. The role delivers secure, grounded, and supportable conversational search using Lucidworks Fusion or a comparable enterprise search platform, with an initialsingle-turn answer experience followed by multi-turn conversational search.
Translate customer and business needs into scalable index and query pipelines across web, content, commerce, and AI platforms. This contract role is an alternative to external professional services and requires hands-on technical delivery as well as solution leadership.
Responsibilities
Search Platform and Retrieval Engineering
· Design and implement collections, schemas, connectors, index/query pipelines, query profiles, and REST API integrations in Lucidworks Fusion or an equivalent platform.
· Build and tune lexical, semantic, vector, and neural hybrid retrieval, including embeddings, blend weights, thresholds, boosting, filters, facets, synonyms, and fallback behavior.
· Use signals and behavioral data to improve relevance, recommendations, personalization, and popular or successful results.
· Create relevance benchmarks, automated regression tests, citation and grounding tests, latency targets, and per-query cost measures.
· Troubleshoot ingestion, indexing, Solr, Kubernetes/AWS, APIs, pipelines, model endpoints, and front-end integrations.
Conversational Search and Generative AI
· Engineer single-turn and multi-turn search flows with intent recognition, entity extraction, query rewriting, clarification, and session context.
· Implement grounded RAG that retrieves approved sources, cites evidence, and suppresses or falls back when evidence or confidence is insufficient.
· Integrate with enterprise LLM services and Corning's AI Gateway using prompt controls, model configuration, rate limits, error handling, latency budgets, and cost governance.
· Tune experiences for product numbers, specifications, availability, certificates, manuals, application notes, and related support content.
· Apply guardrails for transactional or product-SKU queries, low-confidence grounding, and zero-result scenarios.
Integration, Delivery, and Operations
· Partner with AEM and front-end engineers to deliver accessible search results, conversational answers, follow-up suggestions, and facets aligned with the Corning Design System.
· Manage Fusion configuration as code through GitLab, peer review, automated testing, and CI/CD practices.
· Instrument click-through, zero-result, no-click, reformulation, abandonment, task completion, latency, and pipeline-health metrics.
· Produce architecture diagrams, deployment documentation, runbooks, configuration standards, and knowledge-transfer materials.
· Work with security, privacy, legal, architecture, product, content, and business teams on access controls, data handling, AI guardrails, and release readiness.
· Participate in Corning's AI-DLC and Agile delivery cadence.
Required Education, Experience, and Skills
· Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related discipline, or equivalent practical experience.
· 5+ years in search engineering, information retrieval, or enterprise application development, with substantial experience in Lucidworks Fusion or comparable platforms such as Solr/Lucene, Elasticsearch/OpenSearch, Algolia, Vespa, or Azure AI Search.
· Hands-on knowledge of schemas, analyzers, tokenization, synonyms, faceting, boosting, filtering, relevance scoring, query debugging, connectors, signals, custom pipeline stages, and REST APIs.
· Practical experience with semantic search, embeddings, approximate nearest-neighbor retrieval, neural hybrid ranking, relevance evaluation, RAG, grounding, prompt design, citations, guardrails, and fallback patterns.
· Experience with multi-turn context, session design, intent classification, and entity extraction.
· Experience deploying or operating search workloads in AWS and Kubernetes, including observability and production troubleshooting.
· Proficiency in Java and/or Python, JSON, HTTP APIs, Git, automated testing, and CI/CD.
· Ability to convert customer journeys and business requirements into technical designs, backlog items, acceptance criteria, and measurable outcomes.
· Clear communication with technical and nontechnical stakeholders; able to work independently, prioritize competing work, resolve ambiguity, and transfer knowledge.
Preferred Qualifications
· Lucidworks Fusion 5.x implementation, upgrade, or administration experience in a self-hosted environment.
· Experience with Fusion AI, RAY, Learning-to-Rank, Relevance Workbench, Analytics Studio, Commerce Studio, A/B testing, or equivalent capabilities.
· Integration experience with AEM as a Cloud Service, commerce, product catalogs, digital assets, or certificate/document services.
· Knowledge of Redis or another session store; multilingual search; part-number/SKU handling; permissions-aware retrieval; and structured/unstructured content blending.
· Experience in compliance-sensitive or document-intensive environments, plus familiarity with accessibility, privacy, secure AI development, responsible AI, and production monitoring.
· Relevant search, cloud, or AI certifications.
Key Deliverables and Outcomes
· Production-ready architecture for lexical, semantic, hybrid, and conversational search.
· Grounded conversational pipelines that retain context, identify intent and entities, cite evidence, and use appropriate fallback behavior.
· Relevance benchmarks and regression tests for priority customer journeys and product-search patterns.
· Operational dashboards and alerts for quality, adoption, latency, failures, and model or gateway dependencies.
· Runbooks, deployment documentation, standards, and knowledge transfer that enable sustainable ownership.
Success Measures
· Measure
· Expected Outcome
· Search relevance
· Improved judged relevance for priority queries without unacceptable latency or regression.
· Conversational quality
· Users complete multi-turn journeys with retained context and evidence-backed responses.
· Trust and safety
· Answers use approved sources, include citations, are monitored, and are suppressed when confidence is inadequate.
· Customer outcomes
· Reduced zero-result, reformulation, and abandonment rates; improved click-through and task completion.
· Operational readiness
· Pipelines, integrations, and model dependencies are observable, supportable, documented, and recoverable.
Core Competencies
Search engineering | Relevance optimization | Conversational AI | RAG and grounding | Systems integration | Production operations | Analytical problem solving | Cross-functional collaboration | Technical leadership
