AuxoAI is hiring a Senior Data Scientist with strong expertise in AI, machine learning engineering (MLE), and generative AI. You will play a leading role in designing, deploying, and scaling production-grade ML systems — including large language model (LLM)-based pipelines, AI copilots, and agentic workflows. This role is ideal for someone who thrives on balancing cutting-edge research with production rigor and loves mentoring while building impact-first AI applications. Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office) Responsibilities: Own the full ML lifecycle: model design, training, evaluation, deployment Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines Build agentic workflows for reasoning, planning, and decision-making Develop both real-time and batch inference systems using Docker, Kubernetes, and Spark Leverage state-of-the-art architectures: transformers, diffusion models, RLHF, and multimodal pipelines Collaborate with product and engineering teams to integrate AI models into business applications Mentor junior team members and promote MLOps, scalable architecture, and responsible AI best practices Requirements 5+ years of experience in designing, deploying, and scaling ML/DL systems in production Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines Familiarity with agent-based development (e.g., ReAct agents, function-calling, orchestration) Solid understanding of MLOps: Docker, Kubernetes, Spark, model registries, and deployment workflows Strong software engineering background with experience in testing, version control, and APIs Proven ability to balance innovation with scalable deployment B.S./M.S./Ph.D. in Computer Science, Data Science, or a related field Bonus: Open-source contributions, GenAI research, or applied systems at scale