Senior Data Scientist

We are seeking a highly skilled and motivated Data Scientist (LLM Specialist) to join our AI/ML team. This role is ideal for an individual passionate about Large Language Models (LLMs), workflow automation, and customer-centric AI solutions. You will be responsible for building robust ML pipelines, designing scalable workflows, interfacing with customers, and independently driving research and innovation in the evolving agentic AI space.

Key Responsibilities:

• LLM Development & Optimization: Train, fine-tune, evaluate, and deploy Large Language Models (LLMs) for various customer-facing applications.
• Pipeline & Workflow Development: Build scalable machine learning workflows and pipelines that facilitate efficient data ingestion, model training, and deployment.
• Model Evaluation & Performance Tuning: Implement best-in-class evaluation metrics to assess model performance, optimize for efficiency, and mitigate biases in LLM applications.
• Customer Engagement: Collaborate closely with customers to understand their needs, design AI-driven solutions, and iterate on models to enhance user experiences.
• Research & Innovation: Stay updated on the latest developments in LLMs, agentic AI, reinforcement learning with human feedback (RLHF), and generative AI applications. Recommend novel approaches to improve AI-based solutions.
• Infrastructure & Deployment: Work with MLOps tools to streamline deployment and serve models efficiently using cloud-based or on-premise architectures, including Google Vertex AI for model training, deployment, and inference.
• Foundational Model Training: Experience working with open-weight foundational models, leveraging pre-trained architectures, fine-tuning on domain-specific datasets, and optimizing models for performance and cost-efficiency.
• Cross-Functional Collaboration: Partner with engineering, product, and design teams to integrate LLM-based solutions into customer products seamlessly.
• Ethical AI Practices: Ensure responsible AI development by addressing concerns related to bias, safety, security, and interpretability in LLMs.

• Experience: experience in ML, NLP, or AI-related roles, with a focus on LLMs and generative AI.
• Programming Skills: Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch
• LLM Expertise: Hands-on experience in training, fine-tuning, and deploying LLMs (e.g., OpenAI’s GPT, Meta’s LLaMA, Mistral, or other transformer-based architectures).
• Foundational Model Knowledge: Strong understanding of open-weight LLM architectures, including training methodologies, fine-tuning techniques, hyperparameter optimization, and model distillation.
• Data Pipeline Development: Strong understanding of data engineering concepts, feature engineering, and workflow automation using Airflow or Kubeflow.
• Cloud & MLOps: Experience deploying ML models in cloud environments like AWS, GCP (Google Vertex AI), or Azure using Docker and Kubernetes.
• Model Serving & Optimization: Proficiency in model quantization, pruning, distillation, and knowledge distillation to improve deployment efficiency and scalability.
• Research & Problem-Solving: Ability to conduct independent research, explore novel solutions, and implement state-of-the-art ML techniques.
• Strong Communication Skills: Ability to translate technical concepts into actionable insights for non-technical stakeholders.
• Version Control & Collaboration: Proficiency in Git, CI/CD pipelines, and working in cross-functional teams.

• Bachelor’s in Computer Science, Machine learning, or related discipline.Master’s preferred
• Strong background in statistics, machine learning, deep learning and programming necessary. 5+years experience required
• Experience in solving large-scale real-world industry problems, preferably in collaboration with cross-functional, multi-disciplinary teams
• Knowledge of statistical programming techniques and languages (e.g., R, Python, Java, etc.)
• Working knowledge of common machine learning and deep learning approaches (e.g. regression, clustering, classification, dimensionality reduction, supervised and unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning) and data analysis packages (e.g. scikit-learn, pyclustering, pathways analysis, MLlib)
• Prior experience with Tensorflow
• Prior experience in Natural Language Processing using NLTK
• Retail industry experience desired
• Experience using cloud compute (e.g. Google Cloud Platform, AWS, Azure)
• Familiarity with NoSQL databases, graphical analyses, and large-scale data processing frameworks (e.g. Apache Spark)
• Solid understanding of data structures, software design and architecture
• Ability to work independently and take initiative, but also a co-operative team player