Location: NoidaType: Full-TimeExperience Level: 2–5 yearsIndustry: Artificial Intelligence, Machine Learning, Data Science About the RoleWe are looking for a self-motivated Mid-Level Data Scientist to join our AI team focused on GenAI applications. We work at the intersection of multi-modal modeling, Retrieval-Augmented Generation (RAG), and real-time machine learning systems. You’ll collaborate with a high-impact team to design, prototype, and deploy next-generation AI solutions, especially around document understanding and multi-modal tasks .Key ResponsibilitiesDesign and implement state-of-the-art GenAI solutions, involving multi-modal, document understanding models and agents.Build and optimize RAG pipelines, including knowledge of various RAG architectures.Develop and maintain agentic workflows using tools like LangGraph, LangChain.Work with large-scale datasets and ensure efficient data processing pipelines.Perform statistical analysis, algorithm development, and performance tuning.Working with opensource LLMs and deploying them on serving frameworks such as sglang and vllm.Stay up to date with the latest developments in GenAI and ML, and actively contribute to knowledge sharing. Required QualificationsBachelor’s degree (Master’s preferred) in Computer Science, Data Science, AI/ML, or a related field.Minimum 3 years of experience working in machine learning, data science, or AI roles.Strong command of Python and familiarity with R or other scripting languages.Hands-on experience with deep learning, transformer-based models, and multi-modal learning.Proficiency in AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers).Strong understanding of statistics, linear algebra, and probability theory.Experience working with cloud environments, preferably Azure.Exposure to OpenAI, Anthropic, Mistral, or similar APIs and deployment of open-source models (LLaMA, MPT, etc.).Demonstrated experience in document AI, vision-language models, or OCR-based understanding systems. Preferred SkillsExperience with LangGraph, CrewAI, Autogen, or similar orchestration frameworks.Working knowledge of vector databases (e.g., Qdrant, Weaviate, Pinecone) and embedding search techniques.Exposure to Kubernetes, Docker, or ML model deployment workflows.Curiosity-driven mindset with a passion for learning and experimenting with the latest in AI research. Why Join Us?Be part of a team working on powerful AI applicationsAccess to cutting-edge tools and open modelsFlexible working hoursSupportive environment that encourages innovation, research, and upskilling