Senior Data Scientist

About the Role We are seeking an experienced Senior Data Scientist with 5-8 years of relevant professional experience in AI ML systems Candidates must have led at least three end to-end AI ML projects involving CNN ANN model development RAG pipeline integration knowledge graph architecture and LLM-powered applications You will lead the design development and deployment of production-grade AI systems including agentic AI solutions leveraging MCP LLM integrations and multi-modal AI pipelines This is a leadership role where you will also mentor junior engineers and define the technical direction of projects Key Responsibilities Design develop and deploy advanced AI ML models including CNNs ANNs and transformer-based architectures Architect and implement knowledge graph solutions to enable semantic reasoning and complex query answering Lead the design and optimization of RAG pipelines integrating LLMs with vector databases enterprise data sources and APIs Develop agentic AI systems using MCP LangChain AutoGen or similar ecosystems for autonomous workflows Conduct applied research on multi-modal AI graph embeddings and tool-using LLM architectures Integrate AI models with external APIs vector DBs PGVector Pinecone Weaviate FAISS and cloud-based deployments Apply prompt engineering and model fine-tuning techniques for domainspecific applications Ensure scalable maintainable Python codebases for AI services and backend integrations Mentor junior AI ML engineers and provide technical leadership in AI architecture decisions Keep abreast of emerging trends in AI knowledge graphs MCP and large-scale neural architectures Requirements Experience 5-8 years of professional experience in data science AI ML engineering or applied machine learning At least 3 completed large-scale AI ML projects in a professional setting involving CNN ANN RAG knowledge graphs and LLM integrations Prior experience delivering RAG pipelines knowledge graph systems and LLM-powered applications in production environments Must-Have Proven industry experience as a Data Scientist or Machine Learning Engineer working on production-grade AI systems Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow Hands-on experience with CNN ANN model development fine-tuning and deployment Expertise in knowledge graph design Neo4j RDF SPARQL graph embeddings for reasoning and search Demonstrated experience building RAG pipelines and integrating LLMs with structured unstructured data sources Familiarity with MCP Model Context Protocol LangChain or AutoGen for building tool-using AI agents Strong understanding of transformers NLP and embeddings Experience integrating ML solutions with REST APIs and cloud platforms AWS GCP Azure Ability to lead complex AI projects from ideation to deployment Nice-to-Have Experience with multi-modal AI vision text architectures Proficiency in containerization Docker Kubernetes for scalable deployment Background in graph neural networks GNNs and hybrid search techniques Familiarity with front-end technologies React Next js for AI product delivery Why us Opportunity to architect next-generation AI systems combining deep learning knowledge representation and agentic AI Hands-on exposure to MCP-based AI ecosystems for autonomous tool-using applications Collaboration with a high-caliber team of AI engineers data scientists and product strategists Impactful contributions to real-world AI applications across multiple domains Continuous learning environment with research-backed production-focused AI development