Architect and lead the development of an enterprise-grade AIOps platform leveraging machine learning, deep learning, and advanced analytics
Design scalable AI pipelines for anomaly detection, predictive analytics, root cause analysis, and intelligent alerting
Collaborate cross-functionally with engineering, DevOps, and IT teams to integrate AI capabilities into existing operational workflows and tools
Evaluate and select appropriate technologies, frameworks, and cloud-native services in Azure and GCP to support real-time data ingestion, processing, and model deployment
Ensure platform reliability and performance, with a focus on scalability, security, and maintainability
Mentor and guide engineering teams on best practices in AI architecture and model lifecycle management
Stay current with emerging trends in AIOps, MLOps, and IT automation

Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, or related field
3+ years of hands-on experience building AIOps platforms and solutions in IT environments
Experience in IT Operations, infrastructure monitoring, incident management, and observability tools
Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data engineering tools (e.g., Spark, Kafka, Airflow) and knowledge graphs
Experience in Python and familiar with key AI/ML libraries (e.g., TensorFlow, PyTorch, HuggingFace)
Experience with cloud platforms such as Azure and Google Cloud Platform (GCP)
Experience with container orchestration technologies like Kubernetes
Hands-on expertise in working with Large Language Models (LLMs) and Generative AI tools

AI Solutions Architect

Key skills