Walk-in || Artificial Intelligence Engineer

Role & responsibilities

• Python
• SQL
• Machine Learning (ML)
• Docker / Kubernetes
• AWS or GCP

Preferred candidate profile

Key Responsibilities: Build and deploy AI/ML solutions, develop scalable AI pipelines, implement MLOps practices, work with cloud platforms (AWS/GCP), and support Generative AI applications.

Key Responsibilities:

• Production Pipelines: Build, operationalize, and maintain automated ML/AI pipelines to deliver real-time and batch model inferences at scale.
• Code & Architecture Reviews: Participate in design reviews, system code reviews, and structured deployment patterns for critical AI applications.
• Cloud Infrastructure Optimization: Manage cloud resources and leverage platforms like Google Cloud Platform (GCP) or AWS to manage model artifacts and data layers efficiently.
• Cross-Domain Integration: Work across multiple AI paradigms, including Generative AI, Supervised Learning, Unsupervised Learning, and Reinforcement Learning based on business needs.
• MLOps & Automation: Integrate CI/CD concepts specifically tuned for AI applications (AIOps/MLOps) to track versioning, prevent model drift, and automate monitoring.