Senior Machine Learning Specialist

Job description

You will be responsible for developing and implementing machine learning solutions that power our AI-native platform. This role requires expertise in custom model development, transformer architectures, and production deployment of ML systems. RESPONSIBILITIES - Design and develop custom machine learning models for specific business use cases. - Implement and optimize transformer architectures for natural language processing tasks. - Develop and maintain ML pipelines for data preprocessing, model training, and inference. - Work with large language models and implement fine-tuning strategies. - Implement retrieval-augmented generation (RAG) systems and optimize their performance. - Collaborate with engineering teams to deploy ML models in production environments. - Monitor and maintain model performance in production, implementing retraining strategies as needed. - Conduct research on emerging ML techniques and evaluate their applicability to our platform. - Mentor junior ML engineers and contribute to best practices within the team. REQUIRED SKILLS AND QUALIFICATIONS - Master's degree or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience. - 5+ years of experience in machine learning development and deployment. - Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, or similar). - Deep understanding of transformer architectures and their applications in NLP. - Experience with large language models and fine-tuning techniques. - Proficiency in implementing and optimizing RAG systems. - Experience with ML model deployment and production monitoring. - Strong understanding of data preprocessing, feature engineering, and model evaluation techniques. - Experience with cloud platforms and ML infrastructure (AWS SageMaker, Google Vertex AI, or similar). - Excellent problem-solving skills and attention to detail. - Strong communication and interpersonal skills. PREFERRED QUALIFICATIONS - Experience with MLOps and ML pipeline orchestration tools. - Knowledge of distributed training and model optimization techniques. - Experience with vector databases and similarity search algorithms. - Familiarity with reinforcement learning and multi-agent systems.