Senior AI/ML Engineer - Generative AI Specialist

Отговорности

• Design and implement scalable, high-performance infrastructure for training and serving large language models (LLMs)
• Develop and optimize model serving pipelines, focusing on low-latency inference and efficient resource utilization
• Create robust APIs and integrate AI capabilities into our existing software stack
• Implement advanced NLP techniques, including but not limited to, few-shot learning, prompt engineering, and Retrieval-Augmented Generation (RAG)
• Lead the fine-tuning and adaptation of pre-trained models to our specific domains and use cases
• Design and implement evaluation frameworks to measure model performance and ensure output quality
• Collaborate with cross-functional teams to identify AI integration opportunities and drive innovation

Изисквания

• University Degree in Computer Science, Machine Learning, or a related field
• 5+ years of experience in machine learning, with a focus on NLP and deep learning
• Extensive experience with PyTorch or TensorFlow, and familiarity with Hugging Face Transformers
• Proficiency in Python and experience with ML ops tools (e.g., MLflow, Kubeflow)
• Strong background in optimizing model inference (e.g., quantization, distillation, ONNX runtime)
• Experience with distributed training and large-scale model serving architectures
• Familiarity with vector databases and embedding techniques
• Solid understanding of software engineering best practices, including version control, CI/CD, and containerization

Preferred Qualifications

• Experience with transformer architectures and attention mechanisms
• Familiarity with reinforcement learning, particularly in the context of language models (e.g., RLHF)
• Knowledge of prompt engineering techniques and in-context learning
• Experience with cloud platforms (AWS, GCP, or Azure) and their ML-specific offerings
• Understanding of AI ethics, bias mitigation, and responsible AI development practices
• Contributions to open-source ML projects or research publications in NLP/ML
• Technical Stack
• Languages: Python, C++ (for optimization)
• Frameworks: PyTorch or TensorFlow, Hugging Face Transformers
•  Infrastructure: Docker, Kubernetes
• Cloud: AWS SageMaker, AWS Bedrock, Azure ML, or Google Cloud AI Platform
• Databases: PostgreSQL, Apache Cassandra, MongoDB, Redis