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Machine Learning Engineer
4 weeks ago
About Maino.ai
Maino.ai is at the forefront of revolutionizing marketing automation through its end-to-end automated, intelligent, and ROI-driven martech platform. Leveraging cutting-edge Machine Learning (ML) and Generative AI technologies, we are committed to providing a one-stop solution that addresses all the marketing needs of organizations. Our mission is to build the world's most reliable and innovative technology platform, transforming the digital marketing sector into a cost-efficient and accessible domain for everyone.
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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- Proven track record of successfully developing and deploying complex machine learning models, including deep learning architectures.
- 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.
- Extensive experience with cloud computing platforms (e.g., AWS, Azure, GCP) and expertise in deploying models using containerization (Docker) and orchestration tools (Kubernetes).
- Familiarity with big data technologies (e.g., Apache Spark) for handling large-scale datasets and distributed computing.
- Proficient in version control systems (e.g., Git) and collaborative coding platforms.
- Exceptional problem-solving skills, strategic thinking, and a proactive approach to staying updated on industry trends. Minimum Experience: 2.5 + Years
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