Sr Data Scientist - AI & ML

AuxoAI Inc.

Bengaluru

Hybride

INR 4 000 000 - 7 000 000

Plein temps

14 jours+

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Résumé du poste

AuxoAI Inc. is hiring a Senior Data Scientist to lead production-grade ML systems, including LLM-based pipelines, AI copilots, and agentic workflows. The role balances cutting-edge research with production rigor and mentorship.

Hybrid work with 3 days in the office across Mumbai, Bangalore, Hyderabad, or Gurgaon. The candidate will own the full ML lifecycle, fine-tune LLMs, build real-time and batch inference systems, and collaborate with product teams to deploy AI applications.

Qualifications

  • 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.

Responsabilités

  • 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.

Connaissances

Python
PyTorch
TensorFlow
JAX
LLM fine-tuning
Vector search
RAG pipelines
MLOps
Docker
Kubernetes

Formation

B.S./M.S./Ph.D. in Computer Science or Data Science

Outils

Weaviate
PGVector
Spark

Description du poste

Bangalore South, India | Posted on 09/15/2026

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
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