Senior AI Engineer

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.