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.