Auxo AI - Senior Data Scientist - AI/ML

Auxo AI; Gurugram District; On-site; INR 2,800,000 - 5,500,000; Full time

Job summary

Auxo AI is seeking a Senior Data Scientist to design, deploy, and scale production ML systems, including LLM pipelines and AI copilots. You will mentor others and drive impact-first AI initiatives.

You will work across CI/CD, MLOps, and real-time and batch inference using Docker, Kubernetes, and Spark, balancing research ambition with production rigor. Join a fast-growing team shaping responsible AI, reusing state-of-the-art architectures and delivering business impact.

Qualifications

• 5+ years of experience designing, deploying, and scaling ML/DL systems in production.
• Proficient in Python and DL frameworks such as PyTorch, TensorFlow, or JAX.
• Experience with LLM fine-tuning, LoRA/QLoRA, vector search, and RAG pipelines.
• Familiarity with agent-based development (e.g., ReAct agents, function-calling).
• Strong MLOps background: Docker, Kubernetes, Spark, registries, deployment workflows.

Responsibilities

• Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection.
• Fine-tune LLMs and implement RAG pipelines for production use.
• 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 to integrate AI models into applications.
• Mentor junior team members and promote scalable, responsible AI practices.

Skills

CI/CD

ML pipelines

Python

PyTorch

TensorFlow

JAX

LLMs

RAG

LoRA/QLoRA

Weaviate/PGVector

Docker

Kubernetes

Spark

ReAct agents

MLOps

APIs

Education

B.S./M.S./Ph.D. in CS/Data Science

Tools

Docker

Kubernetes

Spark

Weaviate/PGVector

Job description

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 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 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