Sproxil uses mobile technology to combat counterfeiting and increase brand equity with innovative, consumer-focused product protection and targeted marketing solutions. Its award-winning Sproxil Defenderâ„¢ technology drives revenue and engages consumers at point of sale through brand assurance, fraud protection, and loyalty rewards. Sproxil Defenderâ„¢ is touted as the most-widely used solution of its kind, deployed by several large companies across ten industries and protecting millions of consumers around the world.
Key Responsibilities
- Collect, clean, and organize data from various internal and external sources
- Design and implement machine learning models for classification, regression, recommendation, NLP, computer vision, or other relevant tasks.
- Build data pipelines for feature extraction, model training, and evaluation.
- Optimize and fine-tune models for accuracy, latency, and scalability.
- Collaborate with cross-functional teams to translate business requirements into AI solutions.
- Deploy models into production using frameworks like TensorFlow Serving, TorchServe, or cloud-based tools.
- Monitor and maintain model performance post-deployment, including retraining and versioning.
- Document models, experiments, and workflows clearly and effectively.
- Stay updated on the latest research and advancements in AI/ML.
Requirements
- Good understanding of prompt engineering
- Good understanding of LangChain/LangGraph for the development of intelligent Agentic AI
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field.
- 2 years of experience in machine learning, deep learning, or AI-related roles.
- Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
- Strong understanding of algorithms, statistics, and data structures.
- Experience with data preprocessing and feature engineering for structured and unstructured data.
- Familiarity with deploying models to production (e.g., using Docker, Flask, FastAPI, or cloud ML services).
- Experience with version control (Git) and collaborative development practices
Method of Application
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