GENAI context engineer

8+ years of experience in knowledge engineering, enterprise search, data engineering, AI engineering, or platform engineering roles.
Experience building enterprise AI search or knowledge platforms in banking, financial services, healthcare, or other regulated industries.
Familiarity with knowledge graphs, ontology modeling, AI observability, and enterprise governance frameworks.
Understanding of responsible AI, groundedness evaluation, and enterprise compliance requirements for GenAI systems.
Hands on experience with Retrieval Augmented Generation (RAG), semantic search, embeddings, vector databases, and enterprise knowledge systems.
Strong programming skills in Python and experience with API based integrations.
Experience with GenAI and retrieval technologies such as:
Azure OpenAI / OpenAI
Azure AI Search
LangChain / Semantic Kernel
Elasticsearch / OpenSearch
Vector databases and embedding frameworks
Experience designing ingestion pipelines, metadata frameworks, chunking strategies, and contextual retrieval systems.
Strong understanding of enterprise data governance, access control, lineage, and permission aware retrieval. Experience integrating enterprise content systems including SharePoint, Confluence, Jira, document repositories, and enterprise APIs.
Familiarity with cloud platforms such as Azure, AWS, or GCP and containerized environments.
Strong analytical, troubleshooting, and problem solving skills.
Excellent communication and collaboration skills with the ability to work across engineering, architecture, governance, and business teams.