Knowledge Engineer

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 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Senior Engineer role in Knowledge Engineering focused on designing, building, and leading significant workstreams for enterprise-scale Knowledge Graph, semantic layer, ontology, and AI knowledge solutions using Databricks lakehouse and AI capabilities. The role leads a substantial knowledge engineering scope within a large program, translates real-world business problems into scalable AI and KG solutions, guides technical direction, and contributes to thought leadership, reusable assets, and delivery standards. The role must bring relevant industry experience across domains such as BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, applying semantic AI, knowledge graphs, LLM grounding, governed data products, lakehouse pipelines, and Databricks engineering patterns to deliver measurable client value.

Key Responsibilities
Engineer Databricks-based knowledge engineering solutions using Delta Lake, Unity Catalog, Databricks SQL, Workflows, MLflow, Feature Engineering, Vector Search, Model Serving, notebooks, jobs, APIs, and cloud storage integrations.
Build lakehouse graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns on Databricks while improving performance, reliability, scalability, and cost efficiency.
Lead the build of Knowledge Graph solutions that transform client data architecture within a large program scope.
Direct the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions, ensuring all components work together seamlessly.
Work with project leaders, delivery leads, client stakeholders, architects, data engineers, AI engineers, product teams, and domain SMEs to create standout graph-powered data and AI offerings.
Develop strong client relationships, earn trust as a key advisor, and explain the business value of semantic layer, ontology, and knowledge graph solutions.
Make the business case for the recommended semantic layer solution and contribute meaningfully to sales, pre-sales, estimation, proposal inputs, and solution shaping activities.
Provide thought leadership on technology trends, innovation opportunities, limitations, risks, delivery concerns, and practical adoption of knowledge graphs, semantic AI, LLM grounding, RAG, and agentic systems.
Design, evaluate, and maintain ontologies, schemas, standards, and reusable engineering patterns, while guiding teams on data model quality and implementation methods.
Lead a team or workstream within a larger program, mentor engineers, review design/code/configuration, and guide adoption of new methodologies, model building techniques, and algorithms.
Collaborate across business and technical teams to drive end-to-end delivery for the assigned scope and demonstrate value to business and technology stakeholders.

Required Qualifications
Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field.
Minimum 3 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 3 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation.
Minimum 3 years of experience designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical workstreams.
Minimum 2 years of experience implementing end-to-end data pipelines for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration.
Minimum 4 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases.
Minimum 2 years of experience leading a team or workstream within a larger program.
Experience collaborating with engineering, research, product, domain, client-facing, and cross-functional teams across multiple time zones.

Required Skills/ Experience
Hands-on experience building Databricks-based knowledge engineering solutions with Delta Lake, Unity Catalog, Databricks SQL, Workflows, MLflow, Feature Engineering, Vector Search, Model Serving, notebooks, jobs, APIs, and cloud storage integrations.
Strong Python, SQL, and Spark/PySpark experience with frameworks and tools such as TensorFlow, PyTorch, SPARQL, SHACL, Apache Airflow, Apache NiFi, and ETL/ELT pipeline tooling.
Practical knowledge of NLP and search techniques including entity extraction, entity resolution, semantic search, prompt engineering, LLM grounding, and enterprise-scale LLM applications.
Ability to design and implement scalable lakehouse graph ingestion, ontology/schema pipelines, semantic layers, vector search/retrieval patterns, RAG pipelines, and governed knowledge services.
Strong collaboration, technical leadership, delivery ownership, documentation, and stakeholder communication skills.

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 certifications such as Data Engineer Professional, Machine Learning Professional, or related lakehouse credentials.
Industry experience in BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, including domain ontologies, data models, compliance needs, and knowledge-driven use cases.
External client-facing consulting experience, proposal support, solution shaping, or pre-sales exposure.
Advanced degree or Ph.D. in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Data Science, or a related discipline broad exposure to diverse ML techniques and agentic systems.