We are looking for a highly skilled Machine Learning (ML) Engineer to join a newly established GenAI solutions development team, aimed at building modern applications utilizing Large Language Models (LLMs). The team will carry out end-to-end projects: from concept, through prototyping, production deployment, and operations.
responsibilities :
GenAI Application Development: Integrating LLM and AI techniques into end-user applications, focusing on performance and user experience.
AI Algorithms: Designing, developing, and optimizing AI algorithms based on LLMs for specific use cases (text generation, chatbots, data analysis, etc.).
Fine-Tuning LLM Models: Customizing LLM models to meet specific business needs.
Building ML Pipelines: Constructing end-to-end ML pipelines (data processing, training, deployment, monitoring).
Performance Optimization: Reducing latency and managing resources in cloud services (AWS, Azure).
Model Deployment: Collaborating with MLOps engineers to deploy models at scale.
Monitoring and Maintenance: Continuously monitoring and retraining models to ensure their accuracy and reliability.
requirements-expected :
Experience: A minimum of 3 years in Machine Learning engineering, with experience in both classical ML methods and language model-based applications.
Technologies: Advanced knowledge of Python, CI/CD, AWS, AI, LLM, and MLOps. Experience with deep learning frameworks (PyTorch, TensorFlow), LLM ecosystems (e.g., LangChain), designing RAG systems, and fine-tuning LLMs.
MLOps: Familiarity with MLOps tools and practices, including version control, CI/CD, containerization, orchestration, Infrastructure as Code, and automated deployment.
Cloud: Experience deploying LLM and AI models on cloud platforms (AWS, Azure) and knowledge of services like Amazon SageMaker JumpStart and Azure OpenAI.
Data Engineering: Working with structured and unstructured data, data cleaning, feature engineering in databases (NoSQL, relational, vector) and data lakes.
Model Optimization: Experience in optimizing models for performance and solving complex engineering challenges.
Education: Bachelor’s, Engineer’s, Master’s, or PhD degree in computer science or a related field, with experience in artificial intelligence.
