Senior Machine Learning Specialist

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.