Posted:1 week ago|
Platform:
Work from Office
Full Time
Role & responsibilities Key Responsibilities Architect and implement AI/ML/GenAI pipelines , automating end-to-end workflows from data ingestion to model deployment and monitoring. Develop scalable, production-grade APIs and services using FastAPI, Flask , or similar frameworks for AI/LLM model inference. Design and maintain containerized AI applications using Docker and Kubernetes . Operationalize Large Language Models (LLMs) and other GenAI models via cloud-native deployment (e.g., Azure ML, AWS Sagemaker, GCP Vertex AI). Manage and monitor model performance post-deployment, applying concepts of MLOps and LLMOps including model versioning, A/B testing, and drift detection. Build and maintain CI/CD pipelines for rapid and secure deployment of AI solutions using tools such as GitHub Actions, Azure DevOps, GitLab CI . Implement security, governance, and compliance standards in AI pipelines. Optimize model serving infrastructure for speed, scalability, and cost-efficiency. Collaborate with AI researchers to translate prototypes into robust production-ready solutions. Shift: 11am-8pm
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