Senior Data Scientist - AI & ML

Job Description 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