Engineer - ML Ops

G42 is seeking an ambitious and creative-minded Engineer specializing in MLOps to support the development of MLOps at Inception. The role involves driving and enabling automated machine learning model deployments while managing the full lifecycle of our ML projects.

Responsibilities:

• Collaborate with Data Scientists, Data Engineers, and Technology Service departments to manage the end-to-end ML lifecycle, including automation of machine learning workflows, code deployments, testing, and data validation processes.
• Build the foundation of MLOps capabilities.
• Design, implement, and manage CI/CD pipelines tailored for machine learning workflows.
• Ensure proper versioning and automated deployment of machine learning models into production, staging, or testing environments.
• Monitor the performance and reliability of CI/CD pipelines and make adjustments as necessary.
• Develop and maintain the infrastructure and tools that facilitate the deployment and monitoring of ML algorithms in production.
• Keep abreast of the latest technology trends to drive standard practices and documentation.

Qualifications:

• Minimum of 3 years experience in setting up and maintaining CI/CD pipelines, preferably in a machine learning context.
• Strong understanding of containerization and orchestration tools (Docker, Kubernetes).
• Familiarity with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).
• Proficient in scripting languages (Python, Bash, Shell).
• Knowledge of ML training data lifecycle, database management (NoSQL, SQL), and distributed systems.

What We Look For:

• A performance-driven individual who adapts well to ambiguity.
• An eagerness to build meaningful collaborations with stakeholders and create customer-centric solutions.
• A bias for action and a passion for innovation in AI.

What Working at Inception Offers:

• Culture: An open, diverse, and inclusive environment.
• Career: Opportunities for learning and growth through structured training and high-tech projects.
• Work-Life Balance: A hybrid work policy.
• Rewards: Competitive remuneration package with various perks, including healthcare and education support.

If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible.