Middle Python Software Engineer - AI solutions (Freelance)

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.