IR
Search Engineer/Tech Lead
Integrated Resources, Inc
- 🇺🇸 United States
- On-site
- Staff / Principal
- 15 hours ago
- AI
- REST API
- Apache Solr
- Kubernetes
- AWS
- Machine Learning
- RAG
- AEM
- GitLab
- CI/CD
- Agile
- Elasticsearch
- OpenSearch
- Algolia
- Azure AI
- Java
- Python
- JSON
- Git
- Ray
- Redis
15 hours ago
Job Description:
Summary
- Lead the design, build, tuning, and operation of the search layer for client.com.
- 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
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 Clients, 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 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.
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’s 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.
- 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.
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. |
Search Engineer/Tech Lead · Integrated Resources, Inc