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.