Digital Realty

Director of AI Solutions & Engineering

Digital Realty

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Lead

Tech Stack

AirflowAzureCloudDockerJavaScriptKubernetesNext.jsPyTorchReactScikit-LearnTensorflow

About the role

  • We are seeking a visionary and technically elite Director of AI Solutions & Engineering to lead the architecture, engineering, and delivery of enterprise-grade AI solutions within our Corporate Data Office and AI team.
  • This role blends deep technical expertise, strategic leadership, and hands-on engineering to deliver next-generation AI applications that leverage the full power of Artificial Intelligence, Machine Learning, and Data Science.
  • You will own the end-to-end architecture and development of AI systems—leveraging the Microsoft Azure stack (Semantic Kernel, Azure Functions, Cosmos DB, Azure AI Search), React-based front ends, advanced evaluation and observability frameworks, and integrated ML workflows.
  • The ideal candidate is a full-stack AI technologist fluent in AI-assisted development tools, capable of designing scalable architectures, leading engineering execution, embedding machine learning experimentation into the build process, and driving data science–driven insights into business solutions.
  • What you'll do
  • AI Architecture & Engineering Leadership
  • Define and own the enterprise AI architecture blueprint to support Retrieval Augmented Generation (RAG), AI agents, machine learning pipelines, and multi-modal AI solutions.
  • Architect and implement end-to-end AI applications using Azure Semantic Kernel, Azure Functions, Cosmos DB, Azure AI Search, and modern front-end frameworks (React/Next.js).
  • Lead integration of AI solutions with enterprise data sources, APIs, and secure cloud/on-prem environments.
  • Design high-availability, secure, and compliant AI systems aligned to enterprise standards.
  • Machine Learning & Data Science Integration
  • Oversee ML model lifecycle management from feature engineering and model training to deployment, monitoring, and retraining.
  • Direct data science initiatives that use predictive modeling, NLP, and statistical analysis to inform and optimize business processes.
  • Guide teams in experiment design, A/B testing, and model performance analysis.
  • Ensure ML/AI models are explainable, ethical, and auditable, aligning with governance and compliance requirements.
  • Full Stack AI Development
  • Direct and contribute to development across the full stack—from prompt engineering and orchestration to data pipelines, model integration, and UI/UX.
  • Build AI evaluation frameworks with automated testing, benchmarking, and real-time performance monitoring.
  • Implement AI Observability solutions for accuracy, latency, drift detection, hallucination monitoring, and bias detection.
  • Integrate AI FinOps practices to measure, manage, and optimize AI infrastructure costs.
  • AI Tooling & Automation
  • Champion the use of AI-assisted coding, testing, and documentation tools to maximize team productivity.
  • Leverage n8n and similar workflow automation platforms to orchestrate AI pipelines and integrate with business processes.
  • Continuously evaluate and adopt cutting-edge AI frameworks, SDKs, and cloud-native services.
  • Technical Strategy & Stakeholder Engagement
  • Partner with product, engineering, and business leaders to define AI/ML roadmaps, project priorities, and success metrics.
  • Communicate architectural decisions and technical trade-offs to C-level executives and technical teams alike.
  • Lead AI engineering and data science standards, governance, and best practices across the organization.
  • What you’ll need
  • Required
  • Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field; PhD preferred.
  • 8+ years of experience in AI/ML engineering, software architecture, or enterprise solution delivery, including at least 3 years in a leadership role.
  • Proven track record architecting and deploying AI solutions at scale using Azure cloud services (Semantic Kernel, Functions, Cosmos DB, Azure AI Search, Azure OpenAI).
  • Expert in full-stack development, including front-end (React/Next.js), backend APIs, and cloud-native architectures.
  • Strong proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and experience with NLP, RAG, and AI agent development.
  • Proficiency in AI orchestration frameworks (Semantic Kernel, LangChain) and AI evaluation/observability platforms.
  • Experience integrating vector databases and graph databases into AI/ML solutions.
  • Hands-on experience with AI workflow orchestration tools like n8n, Airflow, or Prefect.
  • Demonstrated experience applying data science methodologies to business problems.
  • Experience implementing AI cost optimization (AI FinOps) and observability practices.
  • Preferred
  • Experience in multi-agent AI systems, reinforcement learning, or generative AI product delivery.
  • Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and modern DevOps practices.
  • Knowledge of security, compliance, and ethical AI considerations for enterprise deployments.
  • Exceptional communication skills, capable of influencing technical and non-technical audiences.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field; PhD preferred.
  • 8+ years of experience in AI/ML engineering, software architecture, or enterprise solution delivery, including at least 3 years in a leadership role.
  • Proven track record architecting and deploying AI solutions at scale using Azure cloud services (Semantic Kernel, Functions, Cosmos DB, Azure AI Search, Azure OpenAI).
  • Expert in full-stack development, including front-end (React/Next.js), backend APIs, and cloud-native architectures.
  • Strong proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and experience with NLP, RAG, and AI agent development.
  • Proficiency in AI orchestration frameworks (Semantic Kernel, LangChain) and AI evaluation/observability platforms.
  • Experience integrating vector databases and graph databases into AI/ML solutions.
  • Hands-on experience with AI workflow orchestration tools like n8n, Airflow, or Prefect.
  • Demonstrated experience applying data science methodologies to business problems.
  • Experience implementing AI cost optimization (AI FinOps) and observability practices.
  • Experience in multi-agent AI systems, reinforcement learning, or generative AI product delivery.
  • Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and modern DevOps practices.
  • Knowledge of security, compliance, and ethical AI considerations for enterprise deployments.
  • Exceptional communication skills, capable of influencing technical and non-technical audiences.