- 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).