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Job Description & How to Apply Below
We are seeking a highly skilled Data Scientist – Generative AI & LLMs to design, develop, and deploy cutting-edge AI solutions using Large Language Models and Generative AI techniques. The role focuses on building scalable, production-grade AI systems that deliver measurable business impact across multiple use cases.
Key Responsibilities
- Design, develop, fine-tune, and deploy LLM-based solutions for enterprise use cases such as text generation, summarization, classification, search, and conversational AI
- Apply prompt engineering, fine-tuning (PEFT/LoRA), and RAG (Retrieval-Augmented Generation) techniques to improve model performance
- Build and maintain end-to-end ML pipelines, including data preparation, training, evaluation, deployment, and monitoring
- Integrate LLM solutions with APIs, vector databases, and enterprise data sources
- Evaluate model performance, address bias, hallucinations, and implement responsible AI practices
- Collaborate with product, engineering, and business stakeholders to translate requirements into scalable GenAI solutions
- Optimize inference performance, cost, and latency for production environments
Required Qualifications
- 5+ years of experience in Data Science / Machine Learning, with hands-on experience in Generative AI and LLMs
- Strong proficiency in Python and ML/AI frameworks (PyTorch, Tensor Flow, Hugging Face, Lang Chain, Llama Index)
- Experience working with foundation models (OpenAI, Anthropic, LLaMA, Mistral, Gemini, etc.)
- Solid understanding of NLP concepts, transformers, embeddings, and attention mechanisms
- Hands-on experience deploying models in production environments
- Strong SQL skills and experience working with large-scale datasets
Preferred / Good to Have
- Experience with vector databases (Pinecone, FAISS, Weaviate, Chroma)
- Exposure to cloud platforms (AWS, Azure, GCP) and MLOps tools
- Knowledge of LLM evaluation frameworks, observability, and monitoring
- Familiarity with agentic architectures, tool calling, and orchestration frameworks
- Experience with model governance, explainability, and responsible A
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