Senior AI/ML Engineer – Generative AI Specialist

As our Senior AI/ML Engineer, you will be responsible for architecting, developing, and optimizing our generative AI infrastructure. This role requires expertise across the entire machine learning lifecycle, from data preparation and model development to deployment and monitoring in production environments.

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