Senior Data Scientist - LLM Finetuning

We are looking for a Senior Data Scientist with expertise in Large Language Models (LLMs) and Generative AI to lead the design and delivery of end-to-end AI systems that generate high-quality insights from complex unstructured datasets. This role requires strong ownership across the full lifecycle from data preparation and model fine-tuning to RAG pipeline architecture and scalable deployment on AWS. Key Responsibilities • Architect and implement end-to-end LLM-based solutions for insight generation and automation • Lead the design and optimization of Retrieval-Augmented Generation (RAG) pipelines, including ingestion, chunking strategies, embedding generation, indexing, and retrieval tuning • Fine-tune, evaluate, and optimize LLMs using advanced techniques (e.g., instruction tuning, LoRA) • Define and enforce best practices for data preprocessing, cleaning, normalization, and transformation across diverse data sources • Provide hands-on guidance and code/data reviews for existing data scientists • Help the team develop practical intuition for LLM fine-tuning and evaluation • Establish simple, repeatable workflows for experimentation and iteration • Prevent over-engineering and ensure focus on business outcomes • Design scalable and cost-efficient AI/ML solutions leveraging AWS services • Own the development of vector search infrastructure and embedding pipelines • Establish robust evaluation frameworks for LLM outputs (accuracy, relevance, hallucination reduction) • Collaborate cross-functionally with engineering, product, and business stakeholders to translate requirements into AI solutions • Drive MLOps best practices including versioning, monitoring, CI/CD, and performance optimization• Ensure data governance, security, and compliance in all AI workflows Required Skills & Qualifications • Master’s or PhD in Computer Science, Data Science, Machine Learning, or related field (or equivalent experience) • 5+ years of experience in data science, machine learning, or applied AI roles • Experience mentoring or leading data scientists in applied ML/LLM projects • Proven experience designing and deploying LLM-based solutions in production • Hands-on experience fine-tuning open-source LLMs (Llama, Mistral, Qwen) • Experience with LoRA / QLoRA approaches • Ability to work with limited datasets and iterate quickly • Strong understanding of trade-offs between model size, cost, and performance • Strong expertise in RAG architectures and semantic search systems • Deep understanding of NLP, embeddings, and transformer-based models • Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow) • Hands-on experience with vector databases (e.g., FAISS, Pinecone, OpenSearch, Weaviate) • Strong experience with AWS ecosystem (e.g., S3, SageMaker, Bedrock, Lambda, EC2, Step Functions) • Experience building scalable data pipelines and distributed systems • Solid understanding of API design, microservices, and system integration Preferred Qualifications • Experience with RLHF, prompt optimization, and evaluation frameworks for LLMs • Experience with real-time or streaming data pipelines • Knowledge of cost optimization strategies for LLM workloads