AI Platform Engineering Leader

1 week ago


india beBeeDevops Full time

Senior AI Platform EngineerAs a highly skilled platform engineer, you will be responsible for building and operating a scalable and modular AI platform using technologies such as Qdrant, Anyscale, and Ray.The ideal candidate will have 8+ years of experience in platform engineering, with expertise in Python, cloud platforms, and MLOps/AI DevOps tools. They should be able to design and develop core infrastructure to power conversational applications, implement Large Language Model (LLM) operational solutions, and deploy AI services on AWS with Kubernetes.Platform Design and Architecture: Building and operating a highly available and scalable AI platform.Core Infrastructure Development: Developing essential APIs and infrastructure to power conversational applications, AI agents, and analytics tools.LLM Operational Solutions: Implementing workflows for Large Language Models, including inference pipelines, fine-tuning, caching, and evaluation.Deployment & Performance Optimization: Deploying AI services on AWS with Kubernetes, ensuring scalability and resilience while optimizing vector databases and model runtimes.Collaboration, Governance, & Mentorship: Partnering with engineering teams and research teams to deliver production-grade services, establishing governance/security standards, and mentoring junior engineers.8+ years of experience as Platform Engineer (Site Reliability / DevOps Engineer)3+ years in AI/ML platform development (MLOps)Deep expertise in PythonCloud platforms like AWS, GCP, or AzureMLOps/AI DevOps tools like MLflow or KubeflowCI/CD, infrastructure as code (Terraform / CloudFormation)Implementing and optimizing Platforms supporting large language model (LLM) pipelinesScaling & Setting up Vector DB platforms for semantic search and embeddings managementMLOps tools, Ray.io, Anyscale or other distributed orchestration & inference frameworksDeveloping and deploying containerized applications using Docker and KubernetesUnderstanding of LLMOps patterns — model registry, prompt versioning, and feedback loops


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