AI Drug Discovery Fellowship

Centella AI Therapeutics is an AI-native drug discovery company founded by practicing scientists with 30+ years of combined experience. Our platform, Centella CASCADE™, is an end-to-end, disease-agnostic engine that blends generative models, medicinal chemistry to design high-quality, drug-like molecules rapidly and intelligently. We are seeking curious and technically strong fellows who are passionate about building AI that engineers molecules, not just models 

Key Responsibilities:

• Architect and build end-to-end Agentic AI systems across cloud and on-prem. •
• Integrate LLMs, cognitive services, and ML frameworks like Semantic Kernel, LangChain, and AutoGen.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and knowledge stores.
• Enable agents to interpret and act on chemical and biomedical datasets.
• Deploy scalable agents via Kubernetes, serverless, or containerized services.
• Optimize LLMs through prompt engineering, fine-tuning, and caching.
• Monitor system performance using observability and logging tools.
• Iterate agent workflows based on testing, analytics, and scientific insights.
• Collaborate with AI/ML teams and scientists to align with research goals.
• Iterate workflows and maintain documentation following Responsible AI practices

Qualifications:

• 1–2 years of experience in Machine Learning, Deep Learning, or computational drug discovery
• Bachelor’s/Master’s in CS, AI/ML, Bioinformatics, Comp. Biology, ChemE, or related field
• Strong Python + experience with PyTorch/TensorFlow model development
• Exposure to generative models (Diffusion/VAEs/Transformers) - academic or industry
• Experience designing and deploying AI workflows on multi-cloud or hybrid infrastructure
• Familiarity with vector databases, RAG pipelines, and autonomous agent-driven workflows
• Knowledge of multi-agent orchestration, memory management, security, and Responsible AI principles
• Understanding of security and compliance in multi-cloud environments

Logistics:

• Duration: 3 to 6 months
• Type: Onsite
• Location: Hyderabad