As a Machine Learning Operations Engineer, you will play a pivotal role in developing, deploying, and managing machine learning models and large language model (LLM) systems across our diverse portfolio of companies
This position calls for a blend of technical expertise, a passion for innovation, and the ability to work alongside entrepreneurs to drive growth and transform industries
Responsibilities
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.
Must have
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 problem-solving and analytical skills.
Strong communication and collaboration abilities, with a knack for working effectively in a dynamic, team-oriented 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.
Nice to have
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).
