Knowledge Engineer

Project Role : Knowledge Engineer


Project Role Description : Design and structure knowledge frameworks that enable AI systems to reason and make informed decisions. Capture and translate expert and unstructured knowledge into ontologies, knowledge graphs, and semantic models, ensuring accuracy and context for automation and insights. Apply advanced analytics on knowledge graphs to drive problem-solving and actionable insights.


Must have skills : Databricks Unified Data Analytics Platform


Good to have skills : Graph Databases, Neo4j, Data Engineering
Minimum
5 year(s) of experience is required


Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Engineer role in Knowledge Engineering for a strong individual contributor who helps build scalable Knowledge Graph, semantic layer, ontology, and AI-enabled data solutions using Databricks lakehouse and AI capabilities. The role works hands-on across the knowledge graph lifecycle from ingestion through modeling, curation, implementation, integration, and deployment. The role applies current methodologies, generative AI, LLM, multimodal, graph, search, and semantic techniques to practical business problems while collaborating with users, use case representatives, engineers, architects, UI designers, and delivery teams. The role must include industry experience or project exposure in domains such as banking, insurance, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences.
Build Databricks-based knowledge engineering components using Delta Lake, Unity Catalog, Databricks SQL, Workflows, MLflow, notebooks, jobs, Feature Engineering, Vector Search, Model Serving, APIs, and cloud storage integrations.
Implement lakehouse graph ingestion, ontology/schema pipelines, semantic data products, vector and search integrations, LLM grounding layers, and governed access patterns on Databricks.
Build Knowledge Graph components that contribute to transforming a client''s data architecture.
Design, develop, configure, test, and implement AI and semantic solutions that integrate clearly with the broader enterprise system.
Work alongside project teams, delivery leads, engineers, architects, users, use case representatives, and UI designers to deliver assigned components of an end-to-end solution.
Build solid working relationships with client counterparts on the workstream and communicate implementation progress, risks, dependencies, and technical findings clearly.
Help assemble supporting evidence for recommended semantic layer solutions, including design rationale, implementation notes, validation inputs, data quality observations, and test outcomes.
Support sales, solutioning, demos, accelerators, or reusable assets when called upon by providing technical inputs or implementation examples.
Design, evaluate, maintain, and deploy ontologies, schemas, mappings, graph data models, metadata structures, validation rules, and knowledge graph components as needed.
Keep developing skills in cutting-edge Data and AI solutions, especially agentic technologies, generative AI, LLMs, multimodal models, semantic search, graph RAG, and knowledge graph approaches.
Share learnings with the team and help junior engineers understand practical development patterns, engineering standards, and knowledge graph implementation practices.

Required Qualifications
Bachelor''s degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field.
Minimum 2 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, schema design, ontology management, and KG curation.
Minimum 2 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation.
Minimum 2 years designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical contexts.
Minimum 1 year of experience with end-to-end data pipeline implementation for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration.
Minimum 2 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases.
No leadership or commercial ownership requirement at this band team lead exposure is good to have.
Ability to work hands-on as a strong individual contributor while collaborating across project teams and delivery workstreams.

Required Skills/ Experience
Hands-on experience building Databricks-enabled knowledge engineering solutions with Delta Lake, Unity Catalog, Databricks SQL, Workflows, MLflow, notebooks, jobs, Feature Engineering, Vector Search, Model Serving, APIs, and cloud storage integrations.
2+ years of Python, SQL, and Spark/PySpark experience with frameworks and tools such as TensorFlow, PyTorch, SPARQL, SHACL, Apache Airflow, Apache NiFi, APIs, and ETL pipeline development.
Practical experience with NLP and/or search techniques, prompt engineering, LLMs, retrieval-augmented generation, vector search, semantic search, and enterprise-scale AI application patterns.
Ability to formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph solution components on the lakehouse.
Industry experience applying Databricks-enabled knowledge engineering solutions in banking, insurance, healthcare, retail, communications, manufacturing, energy, public services, life sciences, or another relevant domain.
Ability to build Databricks-based components for semantic layer, ontology, graph, vector search, search, retrieval, and LLM grounding use cases.

Good to Have Skills
2+ years of hands-on experience with cloud/data platforms, with Databricks specialization and exposure to AWS, Azure, or GCP in multi-cloud environments.
Databricks certification or hands-on project experience in data engineering, machine learning, lakehouse architecture, or platform engineering.
Team lead exposure or readiness to guide junior engineers on project tasks.
External client-facing consulting experience, including working with client stakeholders, delivery teams, or solutioning teams.
Broad experience in diverse ML techniques, graph RAG, agentic systems, semantic search, entity resolution, multimodal models, explainable AI, and responsible AI practices.
Experience building reusable code components, implementation templates, design notes, demos, proof-of-concept assets, runbooks, or enablement material.


Qualification 15 years full time education