Job Description At Maino.ai, we are seeking a talented AI/ML Engineer with a comprehensive understanding of the ML models ecosystem, focusing on mar-tech applications. This role offers a unique opportunity for someone passionate about pushing the boundaries of technology and thriving in a dynamic environment. Responsibilities: Advanced ML Models: Lead the development of advanced machine learning models, including but not limited to recommendation systems, predictive analytics, and personalization engines, utilizing a wide range of models such as deep learning architectures (e.g., CNNs, RNNs), LLMs, ensemble methods, and reinforcement learning. Technical Collaboration: Collaborate with cross-functional teams, understanding complex business requirements, and architecting AI/ML solutions using state-of-the-art frameworks and tools, such as TensorFlow, PyTorch, scikit-learn, XGBoost, and LightGBM. Innovative Algorithms: Drive innovation by researching, prototyping, and implementing cutting-edge algorithms and techniques for model optimization and feature engineering, including transfer learning, adversarial training, and attention mechanisms. ML Lifecycle Management: Oversee and execute the complete machine learning lifecycle, ensuring excellence in data preprocessing, model training, deployment, and continuous optimization, leveraging tools like MLflow, Kubeflow, and Apache Airflow. Integration Expertise: Collaborate closely with product managers, software engineers, and data engineers to ensure seamless integration of machine learning solutions within our marketing automation platform, utilizing API frameworks and microservices architecture. Technical Problem-Solving: Take ownership of challenging technical problems, applying creative and scalable solutions. Implement model monitoring and evaluation strategies using tools like Prometheus, Grafana, and ELK stack. Knowledge Across ML Models Ecosystem: Stay abreast of the latest advancements in the ML models ecosystem, including transformer architectures (e.g., BERT, GPT), explainable AI techniques, and autoML tools. Qualifications: Model Development Expertise: Proven track record of successfully developing and deploying complex machine learning models, including deep learning architectures. Programming and Framework Proficiency: Proficiency in Python and extensive knowledge of frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, and LightGBM, and familiarity with model deployment tools like TensorFlow Serving or ONNX. Cloud and DevOps Skills: Extensive experience with cloud computing platforms (e.g., AWS, Azure, GCP) and expertise in deploying models using containerization (Docker) and orchestration tools (Kubernetes). Big Data Technologies: Familiarity with big data technologies (e.g., Apache Spark) for handling large-scale datasets and distributed computing. Version Control and Collaboration: Proficient in version control systems (e.g., Git) and collaborative coding platforms. Problem-Solving Skills: Exceptional problem-solving skills, strategic thinking, and a proactive approach to staying updated on industry trends. Minimum Experience: 2.5 + Years