Sr. Specialist ML Ops

3 weeks ago

Muscat, Muscat, Oman Oman Data Park Full time

ROLE OBJECTIVE

The Artificial Intelligence will be at the forefront of our AI/ML initiatives, focusing on the development, deployment, and operationalization of machine learning models at scale. The engineer will play a crucial role in building and maintaining robust, scalable, and secure pipelines for deploying large language models (LLMs) and other AI/ML models in cloud environments. This role requires a deep understanding of cloud-native technologies, model lifecycle management, and the unique challenges associated with deploying and managing LLMs.

RESPONSIBILITIES

  • Design, implement, and maintain scalable, reliable, and automated MLOps pipelines for model training, validation, deployment, and monitoring in cloud environments.
  • Implement best practices for model versioning, experimentation tracking, and reproducibility.
  • Develop and operationalize large language models, ensuring efficient deployment, fine-tuning, and monitoring of performance metrics.
  • Work closely with cloud architects and DevOps teams to integrate ML models into our cloud infrastructure, leveraging cloud-native tools and best practices.
  • Establish continuous monitoring, logging, and alerting systems to ensure models perform optimally in production, including retraining and updating models as needed.
  • Collaborate with data scientists, software engineers, and other stakeholders to align on requirements, define best practices, and ensure seamless integration of ML solutions into the product ecosystem.
  • Implement and enforce security best practices in model deployment and data handling, ensuring compliance with industry regulations.
  • Maintain thorough documentation of MLOps processes, workflows, and tools. Mentor junior team members and contribute to the growth of the MLOps practice within the organization.
  • Stay up-to-date with the latest advancements in MLOps and LLMOps technologies and trends.
  • Mentor and guide junior team members in MLOps and LLMOps best practices.

ROLE Requirements:

  • Bachelor's or master's degree in Computer Science, Computer Engineering, Data Science, AI/ML, or a related field.
  • AI/ML, MLOps, and LLMOps, DevOps certifications are preferred.
  • A minimum of 5 years of experience in MLOps and at least 2 years of experience in LLMOps
  • Expertise in cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
  • Experience with CI/CD pipelines and DevOps practices.
  • Deep understanding of machine learning lifecycle and model development processes.
  • Experience with large language models and natural language processing.
  • Knowledge of distributed computing and big data technologies.
  • Experience with data engineering and pipeline development.
  • Familiarity with model monitoring tools like MLflow, Prometheus, and Grafana.
  • Strong programming skills in Python, with experience in ML libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face.
  • Strong problem-solving skills and the ability to work in a fast-paced, dynamic environment.
  • Excellent communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
  • Passion for learning and staying updated with emerging technologies.
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