Lead Machine Learning Engineer (Personalization & AI Models)
We are seeking a Lead Machine Learning Engineer (Personalization & AI Models) to pioneer the building and optimization of user segmentation, recommendation, and embedding models within our expansive personalization system for a Mobile App.
The focus areas will include multi-vector representations, real-time model inference, and the integration of personalization workflows with cutting-edge technologies including AWS Personalize SDK, PGVector, and RudderStack.
Responsibilities
- Develop and optimize embedding models using sentence-transformer models for user profiles and personalization
- Implement KNN-based recommendation systems for real-time content scoring and ranking
- Utilize AWS Personalize SDK to train and deploy machine learning models that dynamically adapt to user behavior
- Integrate embedding models with Databricks, RudderStack, and AWS services to ensure real-time profile updates
- Fine-tune ML models aimed at boosting revenue predictions, enhancing user engagement, and refining audience segmentation
- Optimize PGVector and Redis for efficient vector-based lookups and caching
- Collaborate with data engineers to devise ML pipelines for robust training, validation, and deployment processes
Requirements
- Strong experience in Machine Learning, Deep Learning, and AI-driven personalization
- Proficiency in Python, PyTorch, and TensorFlow
- Expertise in vector-based search and recommendation systems, including knowledge of KNN, PGVector, Redis
- Hands-on experience with AWS Personalize SDK and SageMaker or similar ML training pipelines
- Familiarity with Databricks, Delta Lake, and Apache Spark for large-scale model training and deployment
- Strong understanding of real-time personalization, A/B testing, and user segmentation models
- Capability to work with event-driven architectures and implement real-time feature engineering
- Fluent English communication skills at a B2+ level
Nice to have
- Knowledge of ML model monitoring, MLOps, and automation of ML pipelines through CI/CD
- Experience with Graph Neural Networks (GNNs) for analyzing user similarity and clustering
- Familiarity with deployment of real-time analytics tools and dashboarding, such as Looker, Tableau, or Snowflake
