Senior Data Scientist Ai Ml

💼 Full-time🗓 2026-07-25

Core

Designing, deploying, and scaling production-grade ML systems including LLM pipelines, AI copilots, and agentic workflows.

Role type

Senior IC machine learning engineer (generative AI)

Builds

Production ML systems, LLM-based pipelines, AI copilots, agentic workflows

Domain

Generative AI, Large Language Models, MLOps

Deliverable

production ML models

Required skills

Python, PyTorch, TensorFlow, JAX, LLM fine-tuning, LoRA/QLoRA, vector search, RAG pipelines, agent-based development, MLOps, Docker, Kubernetes, Spark, software engineering, testing, APIs

Preferred skills

Open-source contributions, GenAI research, applied systems at scale

Technologies

Docker, Kubernetes, Spark, Weaviate, PGVector

Responsibilities

Own the full ML lifecycle from design to 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 and decision-making; Develop real-time and batch inference systems; Collaborate with product and engineering teams to integrate AI models; Mentor junior team members and promote MLOps and responsible AI best practices

Seniority

Senior, hands-on IC

Rewrite

## About the role 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/Bengaluru/Hyderabad/ Gurgaon (Hybrid) ## 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

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