AI-Research Scientist

Role Overview We are looking for an AI Research Scientist to lead the development of advanced AI/ML. This role focuses on: training and fine-tuning large-scale AI models developing domain-specific AI/ML modules bridging research and real-world applications building scalable, production-ready AI systems You will work at the intersection of machine learning, scientific data, and real-world deployment, contributing to the development of next-generation AI systems for drug discovery. What Youll Work On Designing and training machine learning and deep learning models for complex scientific problems Fine-tuning large-scale models for domain-specific applications Developing custom AI/ML modules tailored to biomedical and drug discovery workflows Building scalable training pipelines and experimentation frameworks Working on LLM-based and generative AI systems for knowledge discovery and reasoning Designing data pipelines for large-scale model training and evaluation Collaborating with engineering teams to deploy models into production systems Continuously improving model performance, robustness, and scalability Key Responsibilities Design, train, and fine-tune advanced ML/DL models Develop domain-specific AI models for structured and unstructured scientific data Build and maintain scalable training and evaluation pipelines Conduct experiments and iterate on model architectures and approaches Work on generative AI, LLMs, and advanced modeling techniques Collaborate with ML engineers and backend teams for production deployment Ensure reproducibility, performance, and reliability of AI systems Stay up-to-date with latest research and translate it into applied solutions Tech Stack Core ML/DL: PyTorch, TensorFlow, JAX (preferred) Data: NumPy, Pandas, large-scale data pipelines AI Systems: LLMs, generative models, domain-specific architectures Infrastructure: Distributed training, GPUs, cloud platforms Backend Integration: FastAPI / Django (for model serving) Cloud: AWS (primary), Azure, GCP Other: Experiment tracking, model versioning, Docker Core Skills Strong foundation in machine learning, deep learning, and statistical modeling Proven experience in training and fine-tuning large-scale models Experience developing domain-specific AI/ML systems Research & Applied AI (Critical) Ability to translate cutting-edge research into real-world systems Strong understanding of generative AI, LLMs, or advanced ML techniques Experience designing novel approaches or improving existing architectures Experience building scalable training pipelines and ML systems Understanding of model deployment and productionization Ability to work with large datasets and compute-intensive workloads Nice to Have: Experience in life sciences, drug discovery, or scientific datasets Nice to Have: Exposure to graph-based models, multimodal learning, or simulation-integrated AI Nice to Have: Publications in relevant AI/ML or computational science domains Nice to Have: Experience with distributed training and optimization Eligibility PhD in Computer Science, AI, Machine Learning, Computational Biology, or related field 4-5 years of relevant experience in AI/ML research and applied systems Strong track record of model development, research, or applied AI work .