We are seeking a Senior Machine Learning Engineer who is passionate about building state-of-the-art recommender systems and leveraging Generative AI. You'll work with large-scale data using tools like Databricks and Spark, contributing to innovative AI solutions that enhance personalized experiences while being part of a supportive, dynamic, and collaborative team. In return, you will be rewarded with an amazing team that supports you, a rich culture, shared success, and the flexibility to work– from the comfort of your home.

Functional Responsibilities:

• Design and implement recommender systems to improve product discovery and enhance customer engagement across digital and physical platforms.
• Build and manage scalable machine learning pipelines for data processing, feature engineering, model training, and deployment using tools like Databricks and Spark.
• Apply and optimize advanced machine learning models for recommendation systems, including Wide & Deep models, Two-Tower architectures, Transformer-based models (e.g., NRMS), embeddings-based approaches, neural networks, autoencoder-based models (e.g., AutoRec), and deep sequential models like GRU4Rec.
• Collaborate closely with software engineers, data scientists, and business stakeholders to integrate models into production systems and solve real-world business challenges.
• Monitor, maintain, and continuously enhance deployed models to ensure reliability, accuracy, and alignment with evolving business needs.
• Stay informed on the latest advancements in machine learning, recommender systems, deep learning, and Generative AI to drive innovation and improvement.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• 5+ years of proven experience as a Machine Learning Engineer, demonstrating successful development and deployment of Machine Learning models.
• Minimum 1 year of hands-on experience designing, building, and deploying recommender systems. This is a must-have requirement.
• Strong programming skills in languages such as Python along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.S
• olid understanding and application of machine learning techniques relevant to recommendation systems, including but not limited to Wide & Deep models, Two-Tower models, Transformers, embeddings, neural networks, autoencoders (AutoRec), and deep sequential models (GRU4Rec)
• Extensive experience handling large-scale data processing and analysis using Spark/PySpark within Databricks, including its native platform services.
• Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
• Strong knowledge of experimental design, A/B testing, and performance evaluation metrics for machine learning solutions.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (Docker) is a plus.
• Excellent verbal and written communication skills in English.