Search Engineer/Tech Lead

For further inquiries regarding the following opportunity, please contact our Talent Specialist, Amit at (224) 507-1290 or Tenish at (224) 507-1292 Title: Search Engineer/Tech Lead Location: Charlotte, NC Duration: 6 Months Note: Would like local, but can be remote. MUST BE EAST COAST TIME ZONE. Strong preference for Lucidworks Fusion experience over any other search platforms. Travel Requirement: Limited, as needed. Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered. Job Description: Scope of Position - Lead the design, build, tuning, and operation of the search layer for client. - The role delivers secure, grounded, and supportable conversational search using Lucidworks Fusion or a comparable enterprise search platform, with an initial single-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 1. 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. 2. 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 client'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. 3. 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 client 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 client'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. 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 Working Relationships - Collaborates with Enterprise Architecture, Digital Experience product owners, AEM/front-end engineering, commerce and product-data teams, AI platform teams, cloud operations, cybersecurity, privacy/legal, analytics, content owners, and implementation partners.