Principal Data Scientist

Principal Data Scientist (Gen AI)

Key Responsibilities:

8+ 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.