Building Scalable AI Infrastructure Engineer

1 day ago


visakhapatnam, India beBeeAI Full time

Senior AI Platform EngineerWe are seeking an experienced Senior AI Platform Engineer to join our cloud-based team in India.The ideal candidate will have a strong background in platform design and architecture, with experience in building and operating highly available, scalable, modular AI platforms using technologies such as Qdrant, Anyscale, and Ray.Design and develop a robust AI platform that supports large language model orchestration, vector search, and multi-agent frameworks.Build essential APIs and infrastructure to power conversational applications, AI agents, and analytics tools.Implement workflows for large language models, including inference pipelines, fine-tuning, caching, and evaluation.Deploy AI services on cloud infrastructure with Kubernetes (EKS), Lambda, and ECS, ensuring scalability and resilience while optimizing vector databases and model runtimes for cost and performance.Develop and maintain high-quality software components, including APIs and microservices.Collaborate with cross-functional teams to deliver production-grade, self-healing, and performance-optimized services for AI/RAG pipelines.Establish governance/security standards and mentor junior engineers in AI infrastructure best practices.Partner with engineering teams and research teams to drive innovation and excellence in AI platform development.8+ years of experience as Platform Engineer (Site Reliability/DevOps Engineer) with at least 3+ years in AI/ML platform development (MLOps).Deep expertise in Python, with strong design and debugging skills.Excellent problem-solving, analytical, and communication skills.Proficiency working with cloud platforms and familiarity with MLOps/AI DevOps tools like MLflow or Kubeflow.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 and setting up vector DB platforms such as Qdrant (or other vector DBs like Pinecone, Weaviate) for semantic search and embeddings management.Exposure to MLOps tools, Ray.io, Anyscale, or other distributed orchestration & inference frameworks.Understanding of LLMOps patterns — model registry, prompt versioning, and feedback loops.


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