Sr. Specialist ML Ops

Oman Investment Authority

Design, implement, and maintain scalable, reliable, and automated ML Ops 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 ML Ops processes, workflows, and tools. Mentor junior team members and contribute to the growth of the ML Ops practice within the organization.

Stay up-to-date with the latest advancements in ML Ops and LLM Ops technologies and trends.

Mentor and guide junior team members in ML Ops and LLM Ops best practices.

Skills

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.

Competency

Behavioral

Collaboration

Planning & coordination

Achievement orientation

Personal effectiveness

Collaboration

Core

Integrity & Ethical behavior

Operational excellence

Effective communication

Stakeholder management

Creativity & innovation

Technical

AI/ML Fundamentals

Deep Learning

Programming for AI

Data Processing

Data Engineering

Data Analysis and Visualization

Model Deployment (MLOps)

Natural Language Processing (NLP)

Computer Vision

Generative AI

LLMs (Large Language Models)

LLM Ops

Cloud Computing for AI

API Development and Integration

Frontend/Backend Development with AI

AI Infrastructure and GPU Utilization

Scaling AI Solutions

AI Ethics and Governance

Business Integration of AI

Project Management for AI

AI Strategy and Innovation

Product Development