Job
Description
Medicine moves too slow. At Velsera, we are changing that. Velsera was formed in 2023 through the shared vision of Seven Bridges and Pierian, with a mission to accelerate the discovery, development, and delivery of life-changing insights. Velsera provides software and professional services for: AI-powered multimodal data harmonization and analytics for drug discovery and development IVD development, validation, and regulatory approval Clinical NGS interpretation, reporting, and adoption With our headquarters in Boston, MA, we are growing and expanding our teams located in different countries! What will you do? Train, fine-tune, and deploy Large Language Models (LLMs) to solve real-world problems effectively Design, implement, and optimize AI/ML pipelines to support model development, evaluation, and deployment Collaborate with Architect, software engineers, and product teams to integrate AI solutions into applications Ensure model performance, scalability, and efficiency through continuous experimentation and improvements Work on LLM optimization techniques, including Retrieval-Augmented Generation (RAG), prompt tuning, etc Manage and automate the infrastructure necessary for AI/ML workloads while keeping the focus on model development Work with DevOps teams to ensure smooth deployment and monitoring of AI models in production Stay updated on the latest advancements in AI, LLMs, and deep learning to drive innovation What do you bring to the table? Strong experience in training, fine-tuning, and deploying LLMs using frameworks like PyTorch, TensorFlow, or Hugging Face Transformers Hands-on experience in developing and optimizing AI/ML pipelines, from data preprocessing to model inference Solid programming skills in Python and familiarity with libraries like NumPy, Pandas, and Scikit-learn Strong understanding of tokenization, embeddings, and prompt engineering for LLM-based applications Hands-on experience in building and optimizing RAG pipelines using vector databases (FAISS, Pinecone, Weaviate, or ChromaDB) Experience with cloud-based AI infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes) Experience in model monitoring, A/B testing, and performance optimization in a production environment Familiarity with MLOps best practices and tools (Kubeflow, MLflow, or similar) Ability to balance hands-on AI development with necessary infrastructure management Strong problem-solving skills, teamwork, and a passion for building AI-driven solutions Show more Show less
