Position Summary: As a ML Backend Developer at DeepKeep.ai, you will transform complex research concepts into scalable, robust applications for production environments. This is a hands-on role that requires a deep understanding of Python, backend development, and machine learning technologies and concepts, and the ability to innovate and implement solutions in a commercial setting.

Key Responsibilities:

• Lead the translation of advanced research prototypes into scalable, production-grade software.
• Work closely with data scientists to understand their research and findings, converting these into practical, scalable solutions.
• Design and implement systems that efficiently handle different data types (including vision language tabular and more) and integrate with technologies like transformers.
• Collaborate with cross-functional teams to drive ambitious projects, ensuring the seamless integration of machine learning technologies into our broader product suite.

Who we're looking for:

• A forward-thinking leader with extensive experience in software engineering and machine learning development.
• Ability to transform complex, algorithmic prototypes into scalable, market-ready solutions.
• A strong foundation in understanding statistical concepts and algorithms in machine learning.
• A collaborative team player who thrives in dynamic environments and is adept at sharing knowledge and insights.

Qualifications:

• Minimum 4 years of practical experience in development, at least two of them as with a machine learning focus.
• Exceptional coding skills in Python, with experience in APIs, Kafka, SQL, No-SQL, and other relevant technologies. Strict typing languages are an advantage.
• Knowledge in machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and data processing libraries (e.g., Numpy, Pandas).
• Possess strong problem-solving and critical thinking skills.
• Proficiency in backend development including RESTful APIs, microservices architecture, and cloud platforms (AWS, GCP, Azure).
• Experience with version control systems (e.g., Git), CI/CD pipelines, and containerization technologies (Docker, Kubernetes).
• Understanding of statistical concepts and algorithms used in machine learning.