| o Entity extraction and linking |
| o Entity resolution and disambiguation |
| o Probabilistic pattern matching |
| o Ontology alignment across heterogeneous data sources |
| Implement semantic modeling for complex domains to support reasoning, discovery, and analytics. |
| Agentic Knowledge Base Enrichment |
| Develop agentic AI systems for: |
| o Automated data gap identification |
| o Knowledge base enrichment and validation |
| o Continuous learning and selfimproving graph pipelines |
| Build workflows that combine LLM reasoning with graph traversal and inference . |
| AI/ML & GenAI Systems |
| Design and implement AI/ML pipelines integrating: |
| o Large Language Models (LLMs) |
| o Small Language Models (SMLs) |
| o Reasoning and taskspecific models |
| Build finetuning pipelines , including: |
| o Dataset generation and curation |
| o Training and finetuning (SFT, PEFT, adapters) |
| o Evaluation, benchmarking, and deployment |
| Apply prompt engineering , RAG , and hybrid LLM + Knowledge Graph (GraphRAG) techniques for contextual intelligence. |
| Anomaly Detection & Analytics |
| Develop anomaly detection systems on top of knowledge graph data at scale. |
| Apply graph analytics, embeddings, and ML techniques to detect: |
| o Semantic inconsistencies |
| o Behavioral anomalies |
| o Data quality and relationship drift |
| Data & ML Engineering |
| Build robust data pipelines that ingest, process, enrich, and publish knowledge graph data. |
| Implement scalable ML systems using Python for: |
| o Model development |
| o Training and tuning |
| o Inference and deployment |
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| Technical Skills & Expertise |
| Core AI/ML |
| Strong AI/ML engineering background with deep expertise in: |
| o Python |
| o Model development, training, tuning, and deployment |
| Extensive handson experience with: |
| o Large Language Models (LLMs) |
| o Small Language Models (SMLs) |
| o Generative AI and reasoning models |
| o Text generation, summarization, and semantic search workflows |
| Knowledge Graph Technologies |
| Strong experience with: |
| o Neo4j , GraphDB |
| o RDF, OWL |
| o Cypher , SPARQL |
| Proven ability to implement: |
| o Entity linking and resolution |
| o Semantic search |
| o Relationship mapping and inference |
| GenAI Frameworks & Tooling |
| Experience building GenAI systems using: |
| o LangChain, LangGraph |
| o LlamaIndex |
| o OpenAI / Azure OpenAI |
| o Vector databases such as Pinecone and FAISS |
| MLOps & LLMOps |
| Strong experience in MLOps and LLMOps , including: |
| o MLflow, Azure ML, Datadog |
| o CI/CD automation for ML systems |
| o Observability, logging, and tracing |
| o Model performance monitoring and drift detection |
| Experience deploying and operating AI systems in production environments. |
| Cloud & Scalability |
| Experience building and optimizing AI/ML and graph pipelines either of any on: |
| o Azure |
| o AWS |
| o Google Cloud Platform |
| Strong understanding of distributed systems, scalability, and performance optimization. |
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