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.
