Job Description We’re looking for an experienced AI Architect to help shape and scale our enterprise AI ecosystem. In this role, you’ll design end to end AI/ML architectures, build robust data and platform integrations, and lead the deployment of high impact AI solutions across cloud and hybrid environments. You’ll partner across data science, engineering, cloud, cybersecurity, and business teams to architect and deliver enterprise-grade AI solutions. Your work will span model design, data pipeline architecture, cloud and compute optimization, and implementing MLOps pipelines for deployment and monitoring. You’ll guide responsible AI governance, ensure security and scalability, and help drive AI strategies that deliver real business value. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements • Proven experience architecting full stack AI/ML solutions (models, data pipelines, MLOps). • Hands on expertise with AWS, Azure, or GCP and GPU/compute infrastructure. • Strong knowledge of MLflow, Kubeflow, Databricks, SageMaker, Vertex AI, or Azure ML. • Deep understanding of model governance, monitoring, and responsible/ethical AI. • Ability to architect data ingestion, feature engineering, storage, and processing layers. • Experience implementing CI/CD and MLOps deployment frameworks. • Strong cross functional communication and leadership skills. • Experience with LLMs or generative AI workloads. • AI platform integration with APIs, knowledge bases, and cloud native services. • Familiarity with AI security, compliance, and risk assessment. • Experience mentoring engineering teams.