Ai Ml Engineer

Role & responsibilities :

• Design, develop, and deploy machine learning models and algorithms to predict drug response and generate novel candidate drug molecules.
• Familiarity with cloud-based data storage and analysis solutions is preferred.
• Develop Deep Learning and Generative AI-based workflows for bioinformatic analyses.
• Collect, clean, and preprocess large datasets (omics) to ensure high data quality and facilitate accurate model training and evaluation.
• Train, validate, and optimize machine learning models using state-of-the-art techniques and algorithms.
• Evaluate and measure the performance of machine learning models using appropriate metrics and techniques.
• Stay up-to-date with the latest advancements in machine learning and contribute to research efforts and innovation within the company.
• To maintain the Git Hub repository, version control, and AWS base database maintenance.
• Experience in applying statistical skills, such as identifying distributions, statistical testing, regression, classification, clustering, etc.
• Proficiency in CADD software packages (e.g., Protein-ligand docking, QM, molecular dynamics).
•  Experience with binding free energy estimation methods (e.g., thermodynamic integration).
•  Familiarity with medicinal chemistry datasets and concepts related to cheminformatics and small molecule drug discovery.
• Data visualization (Matplotlib/Seaborn)
• Familiarity with cloud platforms (AWS, GCP, Azure), version control systems (Git), High-performance computing, and CI/CD pipelines.  

Key Skills and Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or data science
• Proven experience as a Machine Learning Engineer or a similar role with expertise in Generative AI, deep learning algorithms
• Strong knowledge of machine learning algorithms, techniques, and frameworks such as Tensorflow, PyTorch, or Scikit-learn.
• Proficiency in programming languages such as Python or R.
• Experience with data preprocessing, feature engineering, and model training.
• Strong problem-solving and troubleshooting skills.
• Excellent communication and collaboration abilities.