The Role :

We are looking for a strong software engineer with good Python fundamentals, comfort working with data, and the ability to learn new technologies quickly.

You do not need several years of AI experience. What matters more is that you can program well, solve problems logically, understand and work with data, work independently, and learn fast.

You will work with our AI and engineering teams on intelligent assistants, AI agents, workflow automation, data extraction, search, and integrations with external systems.

Key Responsibilities :

- Build backend applications and services using Python.

- Work with structured and unstructured data from databases, APIs, documents, and files.

- Integrate applications with APIs, databases, and third-party systems.

- Build AI-powered features using Large Language Models and AI APIs.

- Help develop agentic workflows that retrieve information, use tools, and perform multi-step tasks.

- Break down ambiguous business problems into practical technical solutions.

- Test, debug, and improve applications for accuracy and reliability.

- Learn and apply new AI tools, models, and techniques as required.

Required Qualifications :

- Good proficiency in Python.

- Strong programming, debugging, and problem-solving skills.

- Comfortable working with data, including cleaning, transforming, analysing, and validating it.

- Experience with SQL, databases, REST APIs, JSON, and common data formats.

- Familiarity with tools such as Pandas.

- Familiarity with Git and standard software development practices.

- Ability to read technical documentation and quickly learn unfamiliar technologies.

- Comfortable taking ownership of a problem and working towards a solution independently.

Nice to Have :

- Backend development with FastAPI, Flask, or Django.

- Experience with Docker or cloud deployment.

- Experience with document processing, search, automation, or information extraction.

- Basic understanding of machine learning and data science concepts, such as classification, regression, training and evaluation.

- Exposure to libraries such as scikit-learn, NumPy, or similar tools.

- Exposure to image and multimodal AI, such as image understanding, generation, computer vision, or image-processing workflows.

- Experience working with AI models or APIs that combine text and images.

- Personal AI projects, GitHub repositories, or experimentation with AI tools.