Description :
- 7+ years of deep AI / ML engineering experience with at least 3+ years leading technical teams.
- Strong expertise in computational chemistry, cheminformatics, structural biology, or scientific machine learning.
- Hands-on mastery of :
1. PyTorch / TensorFlow, CUDA
2. Python (scientific stack)
3. GNNs, transformers, multimodal architectures
4. MLFlow, Airflow, Docker, Kubernetes, AWS / GCP / Azure
5. Distributed training frameworks
Experience deploying ML solutions in production at scale - preferably in scientific SaaS or enterprise platforms.Ability to dissect scientific problems and translate them into robust, elegant technical solutions.Preferred Qualifications :
Experience integrating ML models with simulation platforms (MD, docking, QM / MM, FEP).Familiarity with molecular data structures (SMILES, SDF, MOL2, PDB) and chemistry toolkits (RDKit, OpenEye, DeepChem).Background in physics-inspired ML, generative chemistry models, or protein structure modeling.Strong publication or patent history in ML for drug discovery (bonus but not mandatory).(ref : hirist.tech)