AI Infrastructure Engineer

12 hours ago

bangalore, India beBeeInfrastructure Full time

Scalable AI Infrastructure Engineer JobWe are seeking a highly skilled Scalable AI Infrastructure Engineer to join our team. The ideal candidate will have expertise in designing and operationalizing AI infrastructure for deploying and scaling Large Language Model (LLM) solutions in a telecom ecosystem.Key Responsibilities:Architect and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for model training, fine-tuning, deployment, and monitoring.Set up and optimize Graphics Processing Unit (GPU)/accelerator infrastructure for training and inference workloads, balancing cost-performance trade-offs.Design and maintain LLM inference services, supporting both batch and low-latency real-time deployments using industry-standard serving tools.Develop model performance monitoring, logging, and drift detection pipelines with observability frameworks (Prometheus, Grafana, MLflow).Integrate vector databases and ensure efficient retrieval performance.Implement A/B testing, canary rollouts, and model rollback mechanisms.Desired Qualifications:Experience with low-latency inference optimization.Knowledge of streaming data integrations (Kafka, Spark Streaming) commonly used in telecom.Prior exposure to telecom operational systems or legacy platform integration.Familiarity with cost-optimization techniques for AI workloads in high-throughput environments.Technical Skills:Proven track record of managing enterprise-scale AI systems with a focus on reliability, scalability, and cost-efficiency.Proficiency in ML infrastructure tooling (Docker, Kubernetes, MLflow, Airflow).Experience with model serving tools.Deep familiarity with GPU-based workloads, distributed training, and model inference optimization.Knowledge of cloud platform services (AWS Sagemaker, GCP Vertex AI, Azure ML) including deployment and scaling practices.Exposure to monitoring, observability frameworks, and incident management for AI workloads.Work Experience:8+ years of industry experience in MLOps, AI infrastructure, or DevOps for AI systems, with 2+ years in deploying or managing LLM/Gen AI pipelines.Educational Qualification:Master's or Ph.D. in Computer Science, Data Engineering, Machine Learning, or a closely related field. Ph.D. preferred for research-level AI infrastructure optimization.

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