We are seeking a talented Python Engineer with a strong Django and AWS infrastructure foundation. The ideal candidate will be adept at building and maintaining web applications, working with databases, and deploying scalable cloud-based solutions. A background in machine learning and NLP is a plus.

About the project:

The project is the first global platform designed specifically for biologists, aiming to organize the world’s biomedical datasets. We are building a collaborative ecosystem where researchers can easily find, share, and work with data, driving innovation and accelerating scientific discovery. Unlike traditional, isolated research efforts, the product promotes interdisciplinary collaboration by leveraging high-quality datasets and advanced computational tools like Machine Learning and AI. It is set to overcome key challenges in the research community, establish itself as a principled platform adhering to FAIR, Open Science, and Open Source principles, and ultimately become a vital resource for researchers worldwide.

Key Responsibilities:

• Develop and maintain web applications using Django.
• Design and manage the Postgres database.
• Design and implement API for the front-end part.
• Organize large datasets storage using AWS infrastructure.
• Utilize the AWS services to deploy and manage applications.
• Implement search through datasets and their meta information by user questions.
• Collaborate with cross-functional teams to ensure seamless deployment and integration of applications.

Must-Have Qualifications:

• 6+ years of experience in commercial software development.
• 4+ years of experience with Python programming.
• Strong experience with Django, Django ORM, and Django DRF.
• Proficiency in working with Postgres.
• Experience in AWS, including EC2/ECS, VPC, Lambda, Dynamo DB, Cognito, S3/Data Lakes, and API Gateway. Familiarity with boto3.
• Solid understanding of virtualization tools: venv and Docker.
• Ability to design and deploy scalable, secure applications.
• At least Intermediate English level (spoken and written)

Nice-to-Have Qualifications:

• Experience with the Fast API framework.
• Experience with LLMs and RAG approach. Familiarity with vector storage databases (e.g. Redis, PG_Vector), and similarity search.
• Familiarity with the Hugging Face portal and the Transformers SDK.
• Knowledge in unsupervised learning tasks, including data clustering, anomaly detection, and preprocessing.
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience).