Data Scientist (42715)

I am looking for an experienced Data Scientist to build production-ready AI systems and intelligent data products across multiple business domains. You will work on graph-centric AI, knowledge graphs, RAG pipelines, agentic workflows, NLP/LLM solutions, and modern retrieval systems while integrating structured and unstructured enterprise data. The role requires strong Python engineering skills, solid ML fundamentals, hands-on experience with embeddings, vector search, APIs, cloud platforms, and modern AI architectures including graph databases and knowledge graph solutions.

🚀 Project
- design and implementation of end-to-end workflows for AI systems and data products
- development of graph-centric AI solutions including graph theory, graph data science, and knowledge graphs
- work with graph neural networks such as GCN and GAT
- design and implementation of NLP and LLM solutions
- development of semantic engineering, search/retrieval, and RAG solutions
- fine-tuning models and text-to-SQL solutions
- design and implementation of graph and embedding pipelines
- integration of knowledge graph layers
- implementation of retrieval and reasoning components for agentic workflows
- integration of models, structured and unstructured data, graphs, APIs, and enterprise services
- development of AI systems across Finance, Operations, Supply Chain, Engineering, and Investments
- collaboration with architects, tech leads, UX designers, QA, and functional leads
- delivery of production-ready AI and data solutions in an agile environment

🎯 Skills
- strong Python engineering experience (3–6+ years)
- experience with retrieval/search systems including lexical, vector, and hybrid search
- experience with embeddings, ranking, and reranking
- exposure to graph data science and knowledge graphs
- experience with graph databases such as Neo4j
- hands-on experience building RAG, agentic workflows, or similar AI patterns
- familiarity with model evaluation, testing, and observability tools
- solid knowledge of APIs, microservices, and data-centric integrations
- comfort with cloud platforms such as Azure, AWS, or GCP
- experience with CI/CD workflows
- strong data science and machine learning fundamentals
- understanding of modern AI patterns, retrieval systems, and workflow design

💡 Nice to have
- exposure to GPUs and accelerated training/inference
- experience with distributed compute
- experience with performance and cost optimization in cloud environments
- experience with GNNs such as GCN/GAT

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