MLOps Engineer

• Design build and maintain efficient reliable and scalable ML and LLM operations infrastructure.
• Implement robust ML model lifecycle management practices including development testing deployment and monitoring.
• Work closely with data scientists and ML engineers to facilitate the seamless transition of models from experimentation to production.
• Ensure the highest levels of security and compliance are maintained in all ML and LLM operations.
• Optimize model performance and resource utilization to meet the demands of rapidly scaling ventures.
• Stay abreast of the latest developments in ML and LLM technologies and methodologies integrating these innovations to enhance operational efficiency and model effectiveness.

• Proven experience in ML and LLM operations with a strong understanding of ML model lifecycle management.
• Proficiency in Python and experience with ML frameworks like TensorFlow or PyTorch.
• Excellent problemsolving and analytical skills.
• Strong communication and collaboration abilities with a knack for working effectively in a dynamic teamoriented environment.
• Familiarity with CI/CD pipelines automation tools and ML monitoring solutions.
• Knowledge of data engineering principles and practices is highly desirable.
• A Bachelors or Masters degree in Computer Science Engineering Data Science or a related field.
• Minimum of 3 years of relevant experience in machine learning operations.

• Minimum of 5 years of relevant experience in machine learning operations with a preference for candidates who have experience managing large language models.
• Experience with cloud computing platforms (AWS GCP or Azure) and containerization technologies (Docker Kubernetes).

machine learning,operations,analytical skills,python,pytorch,tensorflow,cloud,machine learning models,docker,kubernetes