AI Infrastructure Specialist

1 week ago

thrissur, India beBeeDevops Full time

Job Title:Senior AI Platform DevOps EngineerJob DescriptionOur company is seeking a highly skilled Senior AI Platform DevOps Engineer to design and operate a scalable, modular AI platform using Qdrant, Anyscale, and Ray for LLM orchestration, vector search, and multi-agent frameworks.The ideal candidate will have expertise in Python with strong design and debugging skills, as well as the ability to work independently and lead complex projects with excellent problem-solving, analytical, and communication skills.Key Responsibilities:Design and implement workflows for Large Language Models, including inference pipelines, fine-tuning, caching, and evaluation for open-weight and hosted models.Develop essential APIs and infrastructure to power conversational applications, AI agents, and analytics tools.Deploy AI services on AWS with Kubernetes (EKS), Lambda, and ECS, ensuring scalability and resilience while optimizing vector databases and model runtimes for cost and performance.Requirements:8+ years of experience as Platform Engineer or DevOps Engineer, with at least 3+ years in AI/ML platform development.Deep expertise in Python, with strong design and debugging skills.Ability to work independently and lead complex projects with excellent problem-solving, analytical, and communication skills.Proficiency working with cloud platforms such as AWS, GCP, or Azure and familiarity with MLOps/AI DevOps tools like MLflow or Kubeflow.Hands-on expertise with CI/CD pipelines, model observability, and incident response for AI/ML services.Preferred Qualifications:Experience implementing and optimizing Platforms supporting large language model pipelines with frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, or similar.Hands-on knowledge of Scaling & Setting up Vector DB platforms such as Qdrant for semantic search and embeddings management.Exposure to MLOps tools, Ray.io, or other distributed orchestration & inference frameworks.Experience with developing and deploying containerized applications using Docker and Kubernetes, including Helm charts and automated scaling.Understanding of LLMOps patterns — model registry, prompt versioning, and feedback loops.

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