We are seeking a skilled and motivated Python Engineer with AI experience to join our team. The successful candidate will work on developing and implementing a wide range of AI solutions, creating proofs of concept (POCs) using existing models and frameworks. This role requires a strong background in Python programming, machine learning, and data processing, with a focus on deploying solutions in cloud environments.
Requirements * 3.5+ years of experience with Python. * Solid understanding of AI techniques and frameworks, including NLP, computer vision, and other AI applications. * Experience with large language models (LLMs). * Experience building data processing pipelines using tools such as Apache Airflow. * Experience with SQL and NoSQL databases. * Experience with Azure and AWS. * Experience in vector storage is a plus. * English level — Upper-Intermediate.
Nice to Have: * Proven expertise in Retrieval-Augmented Generation (RAG) models. * Familiarity with frameworks such as LangChain, LlamaIndex, or Haystack
Qualifications: * Bachelor’s degree in Computer Science, Data Science, or a related field. * Proficiency in machine learning frameworks and libraries such as TensorFlow, PyTorch, or Keras. * Excellent problem-solving skills and the ability to communicate technical concepts to non-technical stakeholders. * Experience working in collaborative, cross-functional teams.
Responsibilities * Develop, train, and fine-tune machine learning models for various AI applications, including but not limited to NLP. * Perform data preprocessing and augmentation for training datasets. * Create and iterate on POCs to demonstrate the feasibility and potential impact of AI solutions. * Build and maintain data processing pipelines using tools such as Apache Airflow. * Collaborate with cross-functional teams to understand requirements and deliver effective AI solutions. * Optimize models for performance and efficiency in a production environment. * Work with SQL and NoSQL databases to manage and utilize data effectively. * Deploy and manage models and data pipelines on cloud platforms such as Azure and AWS.
