What you'll do:

• Model Development: Design, develop, and optimize machine learning models for document processing, pose detection, fraud detection, RAG, and other relevant applications.
• Data Processing: Perform data preprocessing, feature engineering, and exploratory data analysis to prepare datasets for modeling.
• Integration: Work closely with software engineers and product managers to integrate ML solutions into our platforms and applications.
• Research & Innovation: Stay up-to-date with the latest advancements in machine learning and AI to ensure our solutions remain cutting-edge.
• Performance Optimization: Evaluate and improve the performance of existing models and algorithms.
• Compliance & Security: Ensure all ML solutions comply with data privacy regulations and maintain high standards of data security.
• Documentation: Document processes, methodologies, and findings for knowledge sharing and future reference.
• Collaboration: Participate in code reviews, team meetings, and contribute to a collaborative work environment.

What makes you a great fit:

• Experience: 3+ years of professional experience in machine learning, data science, or related roles.
  
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  
• Strong understanding of machine learning algorithms, deep learning, and statistical modeling.
• Experience with document processing techniques, including OCR and natural language processing (NLP).
• Hands-on experience with computer vision and pose detection libraries (e.g., OpenCV, MediaPipe).
• Knowledge of Retrieval-Augmented Generation (RAG) and experience integrating retrieval systems with generative models.
• Familiarity with cloud platforms like AWS, GCP, or Azure for deploying ML models.
• Excellent problem-solving and analytical abilities.
• Strong communication skills, both written and verbal.
• Ability to work effectively in a team-oriented, collaborative environment.
• Experience in the health and wellness industry or with health-related data.
  
• Understanding of data privacy regulations such as HIPAA and GDPR.
• Familiarity with MLOps practices and tools, including Docker, Kubernetes, and CI/CD pipelines.
• Experience with big data technologies like Apache Spark or Hadoop.
• Contributions to open-source ML projects or publications in relevant fields.