Search & Elastic Engineer

Architect, deploy, and maintain self-hosted Elasticsearch clusters, including node configuration, replication, and sharding strategies.
Implement backup, recovery, and disaster recovery processes for Elasticsearch indices.
Develop and optimize semantic search capabilities using machine learning models and embeddings, including BERT-based ELSER.
Enhance search relevance through role-based indexing, boosting strategies, and ranking optimization.
Design and lead the development of hybrid search platforms combining lexical retrieval (BM25) and semantic vector retrieval.
Integrate semantic search capabilities with LLMs for query understanding, reranking, and retrieval-augmented generation.
Monitor Elasticsearch cluster performance and implement best practices for scalability, high availability, and observability.
Collaborate with cross-functional teams to integrate Elasticsearch with knowledge management tools and improve user experience.
Develop and maintain Elastic API integrations using Python or Java.
Design and implement search evaluation frameworks and relevance measurement methodologies.
Apply information retrieval principles, including BM25, ranking signals, and relevance metrics.
Leverage knowledge graphs and metadata enrichment to improve search quality and relevance.

Must-Have Skills:
Strong experience in Elasticsearch administration, cluster management, and performance tuning.
Expertise in search relevance algorithms, semantic search, and information retrieval.
Strong experience with semantic search, embeddings, and hybrid search architectures.
Hands-on experience with Elasticsearch backup, recovery, and disaster recovery.
Proficiency in Python or Java for Elasticsearch API integrations.
Experience with BM25, ranking signals, relevance metrics, and search result optimization.
Experience designing search evaluation and relevance measurement frameworks.
Experience integrating semantic search with LLMs for query understanding, reranking, or retrieval augmentation.
Experience architecting hybrid search platforms combining lexical (BM25) and semantic (vector) retrieval.
Good-to-Have Skills:
Experience with BERT-based ELSER and machine learning models for semantic search.
Familiarity with knowledge graphs and metadata enrichment.
Experience integrating Elasticsearch with enterprise knowledge management tools.
Experience implementing role-based indexing and advanced boosting strategies.
Knowledge of observability and scalability best practices for self-hosted Elasticsearch environments.
CGI is an equal opportunity employer. In addition, CGI is committed to providing accommodation for people with disabilities in accordance with applicable legislation. Please let us know if you require reasonable accommodation due to a disability during any aspect of the recruitment process, and we will work with you to address your needs.
Life at CGI:
It is rooted in ownership, teamwork, respect and belonging. Here, youll reach your full potential because

Come join our team, one of the largest IT and business consulting services firms in the world.

• Amazon Simple Storage Service
• Databricks
• Elastic Suite
• ELK
• Healthcare Information Tech.
• Python
• RESTful (Rest-APIs)
• SQL

That same commitment to fairness extends to how we use technology. To support our recruitment team, AI tools may be used to help assess applications though they never replace human judgement. All hiring decisions remain entirely in the hands of our recruitment professionals.