Position Summary:

As a Senior Machine Learning Engineer at DeepKeep.ai, you will be the pivotal force bridging the gap between our talented research team and our proficient engineering squad. Your role involves transforming complex research ideas into scalable, robust machine learning applications that can be deployed in a production environment. This is a hands-on role requiring a deep understanding of machine learning technologies 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.
• Optimize usage of machine learning models, including implementing techniques like early stopping and optimization against adversarial attacks.
• Work closely with data scientists to understand their research and findings, converting these into practical, scalable solutions.
• Design and implement machine learning systems that work efficiently with different data types and integrate with technologies like transformers.
• Collaborate with cross-functional teams to drive forward 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.
• Advanced knowledge in machine learning frameworks (e.g., PyTorch, TensorFlow) and experience in adapting these for modern computing environments (including GPU and distributed computing).
• Expertise in working with specialized machine learning databases and ensuring the high performance of ML applications.
• Hands-on experience with MLOps, capable of architecting and managing large-scale ML systems.
• A strong foundation of 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.
• A dynamic team collaborator: Known for effective teamwork, open knowledge sharing, and excelling in a dynamic environment.

Qualifications:

Minimum 6 years of practical experience in development, at least two of them as a machine learning engineer.

• Exceptional coding skills in Python, with experience in APIs, Kafka, SQL, No-SQL and other relevant technologies. Strict typing languages - advantage.
• Demonstrated experience in leading ML projects from start to finish (pipelines, models, datasets and backend oriented).
• Proficiency 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.
• ML databases and various software such as vector databases - advantage.
• Strong foundation in MLOps, with hands-on experience in designing and managing large-scale ML infrastructures - advantage.
• Understanding of statistical concepts and algorithms used in machine learning.