Director of AI Solutions & Engineering

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.