Search Engineer

Leidos•Gaithersburg, MD

21h•Hybrid

About The Position

Leidos is seeking a Search Engineer to support, enhance, and modernize an enterprise search platform for a Federal customer. This role involves both software development and operations & maintenance of search applications and indexing pipelines built on Apache Solr, Flume, Spark, Linux, Microsoft SQL Server, and AWS OpenSearch. The engineer will ensure day-to-day reliability of indexing and retrieval services by addressing various issues such as indexing failures, data problems, search syntax and relevance, performance degradation, vulnerability remediation, patching, and recurring maintenance. The position also includes supporting bulk indexing operations, synonym refreshes, name normalization, data purges, and SLA restoration. Furthermore, the role will contribute to evolving the platform towards modern search capabilities using AWS OpenSearch, semantic search, vector embedding, hybrid retrieval, and Retrieval-Augmented Generation (RAG). The ideal candidate should possess strong hands-on troubleshooting and operational ownership, combined with practical experience in search engineering, data pipelines, and cloud-based modernization.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field, or equivalent practical experience.
5+ years of experience in software engineering, enterprise search, search platform support, information retrieval, or large-scale data systems.
Hands-on experience supporting and enhancing enterprise search platforms such as Apache Solr, OpenSearch, Elasticsearch, Lucene, or similar technologies.
Experience working in Linux production environments, including troubleshooting, configuration updates, and routine system support.
Strong experience with indexing, schema design, mappings, analyzers, query processing, search relevance tuning, and performance optimization.
Experience supporting batch or large-scale data ingestion and processing pipelines using Spark and related technologies.
Strong programming skills in Java and/or Python.
Working knowledge of Microsoft SQL Server, including the ability to query source data, validate data flows, investigate discrepancies, and analyze indexing or operational metrics.
Experience using log monitoring and observability tools to investigate failures and support operational troubleshooting; exposure to Azure Log Analytics strongly preferred.
Experience troubleshooting distributed systems, indexing pipelines, data synchronization issues, and production search clusters.
Experience supporting SLA-driven operational environments with accountability for service restoration, recurring maintenance, and issue resolution.
Ability to understand and work with validation, comparison, and support tools used to verify data accuracy between source and target systems.
Strong analytical, troubleshooting, and problem-solving skills.
Strong written and verbal communication skills, including the ability to work across engineering, operations, and stakeholder teams.
Work in US eastern time zone, and available to travel to Washington, DC area once a year.
Ability to clear Public Trust Clearance.

Nice To Haves

Experience administering and tuning AWS OpenSearch in production environments.
Experience modernizing legacy search platforms and migrating workloads from Solr to OpenSearch or similar cloud-native search technologies.
Experience with semantic search, vector search, embeddings, hybrid retrieval, and AI-assisted search solutions.
Experience supporting or implementing Retrieval-Augmented Generation (RAG) retrieval pipelines.
Any exposure in integrating knowledge graph with RAG pipeline is preferred.
Familiarity with chunking strategies, metadata enrichment, synonym management, reranking, and retrieval optimization.
Experience measuring and improving search quality using metrics such as precision, recall, MRR, NDCG, and latency.
Experience with Apache Flume, Kafka, or similar ingestion and streaming technologies.
Experience building automation for maintenance operations, data validation, bulk indexing, and support workflows.
Experience in utilizing GenAI in optimizing the O&M processes, continuous improvements and/or driving innovations.
Experience working in regulated, security-sensitive, or government environments.
Experience supporting enterprise document retrieval, knowledge discovery, or content search use cases.
Enterprise search
Knowledge management systems
Document and content retrieval
Search operations and maintenance
Data quality validation and reconciliation
AI-assisted search and retrieval systems
GenAI innovations
Public sector or regulated environments

Responsibilities

Maintain, support, and enhance enterprise search applications and indexing pipelines running on Solr, Spark, Flume, AWS OpenSearch, and Linux-based infrastructure.
Perform operations and maintenance activities including patching, vulnerability management, system administration, monitoring, configuration management, and routine platform support.
Troubleshoot and resolve indexing failures, data issues, query syntax problems, relevance issues, and search performance degradation.
Monitor indexing jobs and platform health to ensure indexing SLAs and search/retrieval SLAs are consistently met.
Fine-tune search configurations, analyzers, synonyms, ranking logic, and index structures to optimize search relevance, indexing throughput, and retrieval performance.
Perform scheduled maintenance activities including weekly, monthly, and biannual data purges, index cleanup, retention-related tasks, and storage optimization.
Support quarterly bulk indexing operations, including large-scale reloads, synonym refreshes, name normalization, and post-load validation.
Work with source data stored in Microsoft SQL Server to investigate ingestion issues, validate upstream data quality, analyze indexing results, and review operational or search-related metrics captured after indexing.
Use SQL to query source and operational data, investigate discrepancies, support troubleshooting, and validate indexing outcomes and SLA-related metrics.
Use Azure Log Analytics and related monitoring tools to analyze logs, investigate failures, identify operational trends, and support root cause analysis for indexing, search, and data-processing issues.
Maintain awareness of Java-based source-to-target data comparison and validation tools, review their findings regularly, and address data quality, synchronization, and accuracy issues between source systems and indexed search platforms.
Investigate and resolve data ingestion, transformation, indexing, and retrieval issues across upstream and downstream systems.
Build and improve automation, scripts, and support tooling that increases reliability, reduce operational overhead, and improve observability.
Support modernization of legacy search capabilities toward AWS OpenSearch, semantic search, vector search, hybrid retrieval, and RAG-enabled solutions.
Document procedures, runbooks, support processes, recurring maintenance activities, and operational findings, and collaborate with cross-functional teams to improve search quality and system stability.