Applied AI Architect
Architect AI Solutions within ServiceChannel products: Design and lead the development of scalable AI/ML/GenAI-enabled product enhancements aligned with business goals and technical requirements. Collaboration and Execution: Work hand-in-hand with Product, Engineering, Data and Devops teams to ensure seamless integration of AI into existing platform capabilities. Strategic Leadership: Define the vision, roadmap, and governance for AI initiatives, including platform selection, tooling, and best practices. Innovation & Evaluation: Stay ahead of emerging trends in AI/ML and GenAI; conduct technical assessments, feasibility studies, and POCs to rapidly test and validate new capabilities. Operational Excellence: Establish and scale MLOps pipelines to support experimentation, model training, deployment, and monitoring in production environments. Data Collaboration: Partner with data engineering and cloud teams to build robust data pipelines and infrastructure for AI workloads. Responsible AI: Champion ethical AI practices by embedding fairness, transparency, explainability, and compliance into the AI lifecycle. Mentorship & Enablement: Guide and mentor technical teams on AI architecture, model operationalization, and real-world scalability. Bachelor's or master's degree in computer science, Data Science, Artificial Intelligence, Engineering, or a related field; PhD preferred. 10+ years of experience in technology roles, with at least 3 years focused on AI/ML architecture or enterprise-scale system design. Demonstrated success in architecting and deploying production-ready AI/ML solutions at scale within complex, data-rich enterprise SaaS products. Deep understanding of machine learning, deep learning, and generative AI technologies and frameworks (e.g., TensorFlow, PyTorch, Hugging Face, LangChain). Strong knowledge of MLOps practices and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Familiarity with retrieval-augmented generation (RAG), vector databases, fine-tuning of pre-trained models, and building voice or AI agents (e.g. protocols such as MCP). Strong proficiency with cloud platforms (Azure - preferred, AWS or GCP) and data infrastructure. Excellent communication and leadership skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
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Applied AI Architect
Architect AI Solutions within ServiceChannel products: Design and lead the development of scalable AI/ML/GenAI-enabled product enhancements aligned with business goals and technical requirements. Collaboration and Execution: Work hand-in-hand with Product, Engineering, Data and Devops teams to ensure seamless integration of AI into existing platform capabilities. Strategic Leadership: Define the vision, roadmap, and governance for AI initiatives, including platform selection, tooling, and best practices. Innovation & Evaluation: Stay ahead of emerging trends in AI/ML and GenAI; conduct technical assessments, feasibility studies, and POCs to rapidly test and validate new capabilities. Operational Excellence: Establish and scale MLOps pipelines to support experimentation, model training, deployment, and monitoring in production environments. Data Collaboration: Partner with data engineering and cloud teams to build robust data pipelines and infrastructure for AI workloads. Responsible AI: Champion ethical AI practices by embedding fairness, transparency, explainability, and compliance into the AI lifecycle. Mentorship & Enablement: Guide and mentor technical teams on AI architecture, model operationalization, and real-world scalability. Bachelor's or master's degree in computer science, Data Science, Artificial Intelligence, Engineering, or a related field; PhD preferred. 10+ years of experience in technology roles, with at least 3 years focused on AI/ML architecture or enterprise-scale system design. Demonstrated success in architecting and deploying production-ready AI/ML solutions at scale within complex, data-rich enterprise SaaS products. Deep understanding of machine learning, deep learning, and generative AI technologies and frameworks (e.g., TensorFlow, PyTorch, Hugging Face, LangChain). Strong knowledge of MLOps practices and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Familiarity with retrieval-augmented generation (RAG), vector databases, fine-tuning of pre-trained models, and building voice or AI agents (e.g. protocols such as MCP). Strong proficiency with cloud platforms (Azure - preferred, AWS or GCP) and data infrastructure. Excellent communication and leadership skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.