Knowledge Engineer

Overview

Lead enterprise-scale knowledge engineering workstreams that use Databricks, knowledge graphs, semantic layers, ontologies, and AI technologies to deliver scalable business solutions.

What you'll do

• Engineer Databricks-based knowledge solutions using Delta Lake, Unity Catalog, SQL, Workflows, MLflow, Vector Search, and Model Serving.
• Build lakehouse graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns.
• Lead the design and implementation of knowledge graphs, ontologies, taxonomies, schemas, and semantic models.
• Guide technical direction, reusable engineering patterns, data model quality, and delivery standards.
• Lead teams or workstreams, mentor engineers, and review designs, code, and configurations.
• Advise stakeholders and contribute to solution shaping, proposals, estimation, pre-sales, and thought leadership.

What you'll need

• At least 12 years of overall experience.
• Bachelor's degree or equivalent in a relevant technical or quantitative field.
• At least 3 years of experience with knowledge graph technologies, ontology management, semantic modeling, and graph curation.
• At least 4 years of experience with relational, object, graph, and vector databases.
• At least 2 years of experience implementing end-to-end AI data pipelines.
• Python, SQL, Spark/PySpark, Databricks, NLP, semantic search, LLM grounding, and RAG experience.
• At least 2 years of experience leading a team or workstream.

Nice to have

• Experience with AWS, Azure, or GCP and Databricks certifications.
• Industry experience in BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences.
• Client-facing consulting, proposal support, solution shaping, or pre-sales experience.
• Advanced degree or Ph.D. in a relevant discipline.

Details

• Location: Bengaluru.
• Required education: 15 years of full-time education.