Engineering Lead - Azure Cloud and AI

One Edge - Engineering Lead JD Position Summary The Engineering Lead is responsible for leading the design, development, delivery, and operational excellence of a cloud-native enterprise software product running on Microsoft Azure. This role provides technical leadership to cross-functional engineering teams, establishes engineering standards and architecture guardrails, and ensures delivery of scalable, secure, reliable, and AI-enabled solutions. The Engineering Lead partners closely with Product Management, Solution Architecture, DevOps, Security, UX, Data, and AI engineering teams to deliver innovative software capabilities that create measurable business value. The role combines hands-on technical leadership with people leadership, mentoring, and strategic technology planning. Key Responsibilities Technical Leadership Product Delivery Cloud Engineering & Operations • Own the overall technical delivery of one or more product domains. Lead architecture and design decisions for cloud-native solutions running on Microsoft Azure. • Ensure adherence to engineering standards, security requirements, and enterprise architecture principles. • Drive software quality through code reviews, design reviews, testing strategies, and engineering best practices. • • Provide technical guidance and mentorship to development teams. Collaborate with Product Managers and Architects to translate business requirements into scalable technical solutions. • • Lead sprint planning, backlog refinement, estimation, and execution activities. • Identify and mitigate technical risks and delivery dependencies. • Drive successful delivery of product releases while balancing quality, cost, and schedule. Lead development of microservices, APIs, event-driven architectures, and distributed systems. • • Ensure solutions are designed for reliability, availability, scalability, and observability. • Partner with DevOps teams to implement CI/CD pipelines and infrastructure automation. • Monitor platform health, performance, security, and operational metrics. AI and Intelligent Solutions Team Leadership Stakeholder Management Required Qualifications Education Experience Technical Skills Software Development • Drive adoption of AI capabilities within products and engineering practices. Evaluate opportunities to leverage Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), Copilot technologies, and machine learning services. • Guide implementation of Azure AI services, agent frameworks, vector databases, AI search, and intelligent workflow automation. • Establish responsible AI practices including governance, security, transparency, testing, and compliance. • Promote AI-assisted engineering practices to improve developer productivity and software quality. • • Build, mentor, and develop high-performing engineering teams. • Foster a culture of accountability, innovation, collaboration, and continuous improvement. • Support hiring, onboarding, performance development, and career growth of engineers. • Encourage knowledge sharing and engineering excellence across teams. • Partner with business stakeholders, product leaders, and technology leadership. • Communicate technical direction, delivery plans, risks, and status effectively. • Influence strategic technology decisions and roadmap planning. Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related field. • • Master's degree preferred. • 10+ years of professional software development experience. • 3+ years leading software engineering teams. • Proven experience delivering enterprise-scale software products. • Experience operating mission-critical applications in cloud environments. • Strong proficiency in one or more modern programming languages such as: ◦ C# ◦ Java ◦ TypeScript ◦ Python Microsoft Azure DevSecOps AI & Data Working knowledge of: Leadership Competencies • Experience with API design and microservices architecture. • Knowledge of modern software design patterns and distributed systems. • Hands-on experience with Azure services including: ◦ Azure App Services ◦ Azure Functions ◦ Azure Kubernetes Service (AKS) ◦ Azure Container Registry ◦ Azure API Management ◦ Azure Storage ◦ Azure SQL Database ◦ Azure Cosmos DB ◦ Azure Service Bus/Event Grid ◦ Azure Monitor and Application Insights ◦ Azure DevOps and GitHub • CI/CD automation • Infrastructure as Code (Terraform, Bicep, ARM) • Security-by-design principles • Automated testing and deployment practices • Cloud observability and monitoring • Generative AI concepts and architectures • Large Language Models (LLMs) • Prompt engineering • Retrieval-Augmented Generation (RAG) • AI Agent architectures • Azure AI Services • Azure AI Search • Azure OpenAI Service • Vector databases and semantic search • Responsible AI principles and governance • AI evaluation and monitoring techniques • Strategic thinking • Technical decision making Success Measures Preferred Certifications Ideal Candidate Profile A successful Engineering Lead combines the mindset of a software architect, the pragmatism of a delivery leader, and the curiosity of an AI innovator. They can lead teams building modern enterprise products on Azure while strategically incorporating AI capabilities to improve both customer experiences and engineering effectiveness. • Influencing without authority • Team development and coaching • Continuous improvement mindset • Customer-centric delivery • Strong communication skills • Cross-functional collaboration • Predictable and high-quality delivery of product roadmap commitments • Platform reliability, security, and performance targets achieved • Engineering productivity and velocity improvements • Reduced technical debt and improved architecture maturity • Successful integration of AI capabilities into products and engineering processes • High employee engagement, retention, and growth within engineering teams • Strong stakeholder satisfaction and business outcomes • Microsoft Certified: Azure Solutions Architect Expert • Microsoft Certified: Azure Developer Associate • Microsoft Certified: Azure AI Engineer Associate • Certified Kubernetes Administrator (CKA) • SAFe, Scrum, or Agile leadership certifications