About the job Senior Machine Learning Engineer Key Responsibilities: Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency. ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference. Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining. Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives. MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and ensure reproducibility. Performance Monitoring: Develop and maintain monitoring tools to track model performance, drift, and reliability in production. Research & Innovation: Stay updated with the latest trends and advancements in AI/ML, and integrate cutting-edge research into business solutions. Required Qualifications & Skills: Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus. Experience: Minimum 5+ years of experience in machine learning, deep learning, and AI model deployment in production environments. Programming: Strong proficiency in Python, with experience in libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Cloud & Infrastructure: Hands-on experience with cloud services (AWS, GCP, Azure) and MLOps tools like Kubeflow, MLflow, or SageMaker. Big Data & Databases: Experience with Spark, Hadoop, SQL, and NoSQL databases for handling large-scale datasets. DevOps & CI/CD: Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines for ML model deployment. Algorithm Development: Strong knowledge of ML algorithms, deep learning architectures (CNNs, RNNs, Transformers), and optimization techniques. Problem-Solving: Strong analytical and problem-solving skills with the ability to design innovative ML solutions for complex business challenges. Excellent Communication: Ability to explain technical concepts to non-technical stakeholders and document ML processes effectively. Preferred Qualifications: Experience with NLP, Computer Vision, or Reinforcement Learning. Hands-on experience with AutoML, hyperparameter tuning, and model interpretability. Experience with real-time ML applications and edge AI. Contributions to open-source ML frameworks or research publications. #J-18808-Ljbffr