About the Role:

We are seeking an AI R&D Scientist with 3 - 5 years of experience in Machine Learning, NLP, and Generative AI to lead research and development initiatives around Large Language Models (LLMs), Small Language Models (SLMs), AI agents, and advanced enterprise AI solutions.

The ideal candidate should possess strong expertise in model training, fine-tuning, prompt engineering, model evaluation, and deployment optimization.

Key Responsibilities :

Research & Innovation :

- Design and execute research initiatives in Generative AI, LLMs, and SLMs.

- Evaluate emerging foundation models and identify enterprise use cases.

- Publish internal research findings and contribute to innovation roadmaps.

- Stay updated with the latest developments in AI research.

Model Development :

- Train, fine-tune, and optimize LLMs and SLMs for domain-specific applications.

- Build custom language models from open-source architectures.

- Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and DPO.

- Develop model evaluation frameworks covering accuracy, reasoning, hallucination rates, and safety.

Prompt Engineering & AI Systems :

- Design advanced prompt engineering strategies.

- Develop agentic workflows and autonomous AI systems.

- Build Retrieval-Augmented Generation (RAG) architectures.

- Create multi-agent systems and orchestration pipelines.

- Improve model reliability, latency, and cost efficiency.

Deployment & Optimization :

- Deploy AI models on cloud and on-premise environments.

- Optimize inference pipelines using quantization, distillation, and model compression techniques.

- Work with GPUs and distributed training environments.

- Collaborate with product and engineering teams to productionize AI solutions.

Required Skills :

Technical Expertise :

- 3 - 5 years of experience in Machine Learning, Deep Learning, or NLP.

- Strong proficiency in Python and AI development frameworks.

- Deep understanding of Transformer architectures.

- Knowledge on ML / DL concept and proficiency in stats.

- Hands-on experience with LLM fine-tuning, SLM development, Prompt engineering, RAG systems, Embedding models, and AI agents.

Frameworks & Tools :

- PyTorch (preferred), TensorFlow (optional), Hugging Face Transformers, LangChain, LlamaIndex, vLLM, Ollama, Vector databases (Milvus, Pinecone, Weaviate, ChromaDB), Docker, and Kubernetes.

Model Training Experience :

- Experience with Llama family models, Mistral, Qwen, Gemma, DeepSeek, and Falcon.

Preferred Qualifications :

- M.Tech/B.Tech/PhD in AI, ML, Computer Science, Data Science, or related fields.

- Experience building production-grade Generative AI applications.

- Research publications, patents, or open-source contributions are highly valued.

- Experience with distributed training, RLHF, DPO, or model alignment techniques.

Success Metrics :

- Delivery of fine-tuned domain-specific AI models.

- Improvements in model accuracy, latency, and cost efficiency.

- Successful deployment of production-ready AI solutions.

- Contribution to innovation, research, and intellectual property creation.

Compensation :

- Competitive salary, performance incentives, research opportunities, and access to cutting-edge AI infrastructure.