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Machine Learning Engineer
2 months ago
Ccube, a leading digital technology service provider, is seeking a highly skilled AI-ML Engineer to join our team. As a key member of our organization, you will be responsible for designing and architecting scalable AI platforms to develop and deploy AI solutions leveraging machine learning techniques and deep learning architectures.
Key Responsibilities- AI Platform Development: Design and architect scalable AI platforms to develop, deploy, and maintain AI solutions leveraging machine learning techniques and deep learning architectures.
- Joint Architecture Design: Collaborate with business stakeholders, data scientists, engineering teams, product, and other key partners to gather functional and non-functional requirements for solving AI use cases on the AI Platform.
- Emerging Technology Evaluation: Evaluate emerging technologies and tools in the AI area and perform fitment analysis to the Enterprise AI Platform and capabilities strategy.
- AI/ML Architecture Best Practices: Define and implement AI/ML architecture best practices, frameworks, and standards.
- AI/ML Infrastructure Setup: Lead AI/ML infrastructure setup, including cloud services selection, data pipelines, and model deployment.
- AI/ML Solution Robustness: Ensure robustness, reliability, and scalability of AI/ML solutions in production environments.
- Data Governance and Security: Design and implement data governance, security, and compliance measures for AI/ML platforms.
- AI/ML Workflow Optimization: Optimize AI/ML workflows for performance, cost efficiency, and resource utilization.
- Technical Leadership: Provide technical leadership and mentorship to AI/ML development teams.
- Communication: Communicate AI/ML architecture decisions and strategies to stakeholders and executives.
- Proven Experience: Proven experience as an AI/ML platform engineer.
- Deep Understanding: Deep understanding of machine learning algorithms, deep learning architectures, models, and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
- Cloud Expertise: Expertise in cloud platforms (e.g., GCP, Azure) and their AI services.
- Model Development Life Cycle: Strong knowledge of model development life cycle, software engineering principles, data engineering principles.
- Containerization and Orchestration: Experience with containerization and orchestration tools (e.g., AKS, GKE, OpenShift Container Platform, Docker, Kubernetes) for deploying AI/ML models.
- Distributed Computing Systems: Ability to design and optimize distributed computing systems for AI/ML workloads.
- DevOps Practices: Familiarity with DevOps practices, CI/CD pipelines, and automation tools in AI-ML contexts.
- Problem-Solving Skills: Excellent problem-solving skills and ability to address complex technical challenges.
- Communication Skills: Effective communication skills to collaborate with cross-functional teams and stakeholders.
Ccube offers various benefits, including subsidized medical, dental, and vision insurance. Actual compensation is influenced by years of experience, specialized skill sets, and unique qualifications.
