Senior Data Scientist - AI & ML

Auxo AI; Hyderabad; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

AuxoAI is hiring a Senior Data Scientist to lead production-grade ML systems, including LLM-based pipelines, AI copilots, and agentic workflows. Hybrid role across Mumbai, Bengaluru, Hyderabad, and Gurgaon with 3 days in-office.

You will design, deploy, and scale end-to-end ML solutions while mentoring teammates and upholding responsible AI practices.

Qualifications

• 5+ years of experience in designing, deploying, and scaling ML/DL systems in production.
• Proficient in Python and DL frameworks (PyTorch, TensorFlow, or JAX).
• Experience with LLM fine-tuning, vector search, and RAG pipelines.
• Familiarity with agent-based development and MLOps.

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 RAG pipelines.
• Build agentic workflows for reasoning, planning, and decision-making.
• Develop real-time and batch inference systems using Docker, Kubernetes, and Spark.
• Collaborate with product and engineering teams to integrate AI models into business apps.
• Mentor junior team members and promote MLOps, scalable architecture, and responsible AI practices.

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

Skills:- Machine Learning (ML), Deep Learning and Generative AI (GenAI)