Core
Building scalable AI systems for pharmaceutical compliance, quality, and trust using LLMs and RAG.
Role type
Junior Machine Learning Engineer (LLM/RAG)
Builds
Intelligent systems that read regulations, understand SOPs, and fix compliance gaps.
Domain
Pharmaceutical compliance and quality assurance
Deliverable
production ML models
Required skills
ML fundamentals (supervised/unsupervised learning, deep learning, NLP, statistics), RAG architectures, LLMs and transformer-based models, PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, Python, NumPy, Pandas, Matplotlib, MLOps concepts, Git
Preferred skills
Docker, Kubernetes, CI/CD for deployment, vector databases (Pinecone, FAISS, Weaviate), Kaggle or open-source contributions
Technologies
PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, Pinecone, FAISS, Weaviate
Responsibilities
Develop, train, fine-tune, and evaluate machine learning and LLM-based models for production; Build and optimize RAG pipelines including data ingestion, chunking, embeddings, indexing, and retrieval; Integrate LLMs with vector databases and backend systems; Implement ML pipelines using MLOps practices; Monitor model performance and manage versioning; Maintain clean, reproducible, and well-documented codebases
Seniority
Junior, early-career IC
