Data Scientist

Role & responsibilities

Design and implement predictive analytics models for forecasting and decision-making. • Develop and train neural network architectures (CNNs, ANNs, transformers) for NLP and AI-driven applications. • Build and maintain knowledge graph solutions (Neo4j, RDF/SPARQL) for semantic reasoning. • Implement and optimize RAG pipelines integrating LLMs with vector databases and enterprise data. • Integrate AI models with APIs, vector DBs (Pinecone, Weaviate, FAISS), and cloud services. • Apply prompt engineering, fine-tuning, and evaluation for LLM applications. • Collaborate with stakeholders to define data requirements, success metrics, and deployment strategies. • Research and apply innovations in MCP (Model Context Protocol) and multi modal AI. • Document model design, experiments, and deployment processes.

Preferred candidate profile

3-5 years of professional experience in data science, AI/ML engineering, or predictive analytics. • At least 2 completed AI/ML projects in a professional setting using CNN/ANN models, RAG pipelines, knowledge graphs, or LLM integrations. Technical Skills (Must-Have): • Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn). • Solid understanding of predictive modeling, statistical analysis, and deep learning. • Knowledge of graph databases (Neo4j, RDF/SPARQL) and semantic reasoning. • Experience with LLM frameworks (LangChain, AutoGen, OpenAI SDK). • Familiarity with vector databases (Pinecone, Weaviate, FAISS). • Knowledge of REST API integration and handling structured/unstructured data. Nice-to-Have: • Exposure to MCP for tool-using AI architectures. • Knowledge of graph neural networks (GNNs) and multi-modal AI. • Cloud deployment experience (AWS, GCP, Azure) or containerization (Docker).