Lead Data Scientist

As a Lead Data Scientist at our company, you will play a crucial role in our AI/ML team by leveraging your deep expertise in Generative AI, large language models (LLMs), and end-to-end ML engineering. Your responsibilities will involve designing and developing intelligent systems using advanced NLP techniques and modern ML practices. You will be a key player in building and optimizing ML pipelines and AI systems across various domains, as well as designing and deploying RAG architectures and intelligent chatbots. Collaboration with cross-functional teams to integrate AI components into scalable applications will be essential, along with providing technical leadership, conducting code reviews, and mentoring junior team members. You will drive experimentation with prompt engineering, agentic workflows, and domain-driven designs, while ensuring best practices in testing, clean architecture, and model reproducibility. To excel in this role, you must possess expertise in AI/ML, including Machine Learning, NLP, Deep Learning, and Generative AI (GenAI). Proficiency in working with the LLM stack, such as GPT, Chatbots, Prompt Engineering, and RAG, is required. Strong programming skills in Python, familiarity with essential libraries like Pandas, NumPy, and Scikit-learn, and experience with architectures like Agentic AI, DDD, TDD, and Hexagonal Architecture are essential. You should be comfortable with tooling and deployment using Terraform, Docker, REST/gRPC APIs, and Git, and have experience working on cloud platforms like AWS, GCP, or Azure. Familiarity with AI coding tools like Copilot, Tabnine, and hands-on experience with distributed training in NVIDIA GPU-enabled environments are necessary. A proven track record of managing the full ML lifecycle from experimentation to deployment is crucial for success in this position. Additionally, experience with vector databases, knowledge of GenAI frameworks like LangChain and LlamaIndex, contributions to open-source GenAI/ML projects, and skills in performance tuning of LLMs and custom models are considered advantageous. If you are passionate about leveraging AI technologies to deliver real-world solutions, we are excited to discuss how you can contribute to our cutting-edge AI/ML team.,