Principal Data Scientist

Perfect Fit:

• 7+ years of experience in data science, including 2+ years of hands-on experience working with and fine-tuning pre-trained LLMs (e.g., GPT, LLaMA) and diffusion models (e.g., Stable Diffusion, DALL-E, or MidJourney).
• Proficient in Python with a solid understanding of supervised and unsupervised learning techniques and strong hands-on expertise with machine learning packages such as scikit-learn, XGBoost, LightGBM, etc.
• Proficiency in deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers for fine-tuning and deploying LLMs and generative AI models.
• Proven experience in designing and implementing pipelines leveraging Vector/Graph Databases (e.g. Pinecone, FAISS, Neo4j) and Retrieval-Augmented Generation (RAG) for large-scale, efficient information retrieval and model integration.
• Experience with prompt engineering to optimize LLM performance for specific tasks, including designing and refining prompts to improve model outputs, enhancing coherence, relevance, and creativity in generated content.
• Hands-on experience with big data technologies such as PySpark for processing and analyzing massive datasets at scale.
• Exceptional problem-solving skills, with a proven ability to tackle complex challenges in language understanding, multimodal AI, and Generative AI projects.

Nice-to-Have:

• Experience leveraging agents in LLMs (e.g., LangChain or similar frameworks) to build dynamic, multi-step workflows for reasoning, planning, and decision-making tasks.
• Familiarity with MLOps/LLMOps principles, including CI/CD pipelines, monitoring tools (e.g., MLflow, Kubeflow).
• Experience with containerization technologies (e.g., Docker, Kubernetes) and serverless architectures for scalable AI deployments.
• Knowledge of RESTful APIs, message brokers (e.g., RabbitMQ, Kafka), and event-driven architectures to build robust, real-time AI applications.
• Hands-on experience with cloud platforms such as AWS or Azure AI for deploying large-scale ML models.
• Knowledge of ethical AI principles, including fairness, transparency, and bias mitigation in AI systems.
• A Masters or Ph.D. in Statistics, Machine Learning, Mathematics, Computer Science, or a related quantitative field.