MLOps Engineer

1 day ago

Mumbai, Maharashtra, India Kezan consulting Full time ₹ 12,00,000 - ₹ 36,00,000 per year
Role Overview 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 Bachelor's or Master's 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).

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