Senior AI Scientist

Luna Bearings; Hyderabad; Sur place; INR 3 200 000 - 5 200 000; Plein temps

Résumé du poste

Luna Bearings is seeking a Senior AI Scientist to lead the development and deployment of advanced AI/ML solutions. You will work on Generative AI, LLMs, NLP, recommendation systems, and agentic AI, bridging research and production.

You will collaborate with software, data, and platform teams to scale AI across the organization. The role requires strong background in ML/DL, hands-on Python, and experience deploying models at scale, with a focus on performance, latency, and cost.

Qualifications

• Master's or PhD in Computer Science, AI, ML, Statistics, Mathematics, or related technical field.
• 3–5 years of experience in AI/ML, Data Science, or related field.
• Strong hands-on experience with Python and ML frameworks (PyTorch or TensorFlow).
• Experience deploying ML models at scale and with Generative AI/LLMs, NLP, or related areas.

Responsabilités

• Design and develop AI/ML solutions across the organization (LLMs, Generative AI, NLP, RAG, personalization).
• Research and evaluate state-of-the-art AI techniques for business problems.
• Develop and fine-tune foundation models and embedding models.
• Design training, fine-tuning, evaluation, and inference pipelines for AI models.
• Collaborate with software, data, and product teams to productionize AI solutions.
• Optimize models for accuracy, latency, and cost.
• Publish internal research or patents when appropriate.

Connaissances

Python

PyTorch

NLP

LLMs

Reinforcement Learning

Distributed Computing

MLOps

Formation

Master's or PhD in CS/AI/ML/Statistics

Outils

PyTorch

TensorFlow

Spark

Docker

Kubernetes

MLflow

FAISS

Description du poste

Role Overview

We are looking for a Senior AI Scientist to drive the research, development, and deployment of advanced Artificial Intelligence and Machine Learning solutions across the organization. The role will focus on solving complex business and product problems using modern AI techniques, including Generative AI, Large Language Models (LLMs), NLP, recommendation systems, deep learning, reinforcement learning, and agentic AI.

The ideal candidate combines strong scientific thinking with hands‑on engineering capabilities and can take AI ideas from research and experimentation through production deployment and continuous optimization.

In This Role You Will

• Design and develop LLM, Generative AI, RAG, NLP, recommendation, personalization, and agentic AI solutions.
• Research and evaluate state-of-the-art AI/ML techniques and determine their applicability to business problems.
• Develop and fine‑tune foundation models, domain‑specific models, embedding models, and task‑specific models.
• Design training, fine‑tuning, evaluation, and inference pipelines for AI models.
• Apply techniques such as transformer architectures, LLM fine‑tuning, RAG, embeddings, vector search, deep learning, reinforcement learning, contextual bandits, and information retrieval.
• Develop experimentation frameworks and establish metrics for model quality, relevance, accuracy, robustness, latency, and cost.
• Investigate model behavior through experimentation, error analysis, ablation studies, and statistical analysis.
• Build prototypes and proof‑of‑concepts and transition successful approaches into production systems.
• Collaborate with Software Engineering, Product, Data Engineering, and Platform teams to productionize AI solutions.
• Optimize models and AI systems for accuracy, scalability, latency, memory, and inference cost.
• Design and implement model evaluation frameworks, including automated, human, and LLM‑based evaluation.
• Stay current with advances in AI research and translate relevant research into practical solutions.
• Publish internal research, technical documentation, patents, or external research papers where appropriate.

You've Got What It Takes If You Have

• Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related technical field.
• 3 to 5 years of experience in AI/ML, Data Science, or a closely related field.
• Strong understanding of machine learning and deep learning fundamentals.
• Strong hands‑on experience with Python and modern ML frameworks such as PyTorch or TensorFlow.
• Experience developing and deploying machine learning models at scale.
• Strong experience with one or more of Generative AI/LLMs, NLP, recommendation systems, information retrieval, deep learning, reinforcement learning, or computer vision.
• Experience with transformer architectures and modern foundation models.
• Experience with model fine‑tuning, embeddings, vector databases, RAG, and LLM evaluation.
• Strong understanding of experimentation, statistical modeling, model evaluation, and hypothesis testing.
• Experience working with large‑scale datasets and distributed computing frameworks such as Spark.
• Strong analytical, communication, and problem‑solving skills.

Extra Dose of Awesome If You Have

• Experience with foundation models such as Llama, Mistral, Gemini, or comparable models.
• Experience with Hugging Face, LangChain/LangGraph, FAISS, Milvus, Weaviate, or similar AI technologies.
• Experience with model optimization techniques such as quantization, distillation, pruning, and parameter‑efficient fine‑tuning (PEFT/LoRA).
• Experience designing production‑grade AI platforms and services.
• Experience with Docker, Kubernetes, cloud AI/ML platforms, and MLOps.
• Experience with MLflow or similar experiment and model management platforms.
• Experience with AI evaluation frameworks and LLM‑as‑a‑Judge approaches.
• Research publications, patents, or contributions to open‑source AI projects.