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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director/Managing Director, AI Software Development - **Company:** CohnReznick LLP - **Location:** Denver, CO, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Microsoft Azure, Cloud Engineering, Cyber Security, Information Systems, Continuous Integration, Data Governance, Iterative and Incremental Development, Machine Learning, Proprietary Software, OpenAI, AI-enabled Products, Microsoft Copilot, Secure Coding, Software Engineering, Systems Integration, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Model Validation, Technical Debt, Generative AI, Agentic-AI, B2b Software, Information Technology, Data Analytics, Data Management, Devsecops - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25303_29449f07987752ac962da73e696dbd4f ## About the Role * Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent relevant experience. * Fifteen or more years of progressive software engineering and technology leadership experience. * Seven or more years leading software development organizations, including leaders of engineering teams. * Five or more years delivering AI-enabled applications, machine learning platforms, intelligent automation solutions, or generative AI products. * Demonstrated success creating and scaling a native AI software development organization that moved beyond pilots and experimentation to secure, production-grade solutions. * Proven delivery of enterprise-grade software products and platforms in complex, regulated, or high-trust environments. * Experience leading geographically distributed teams and a blended workforce of employees, contractors, and strategic partners. Technical and delivery expertise * Generative AI, large language models, agentic architectures, retrieval-augmented generation, AI evaluation, observability, and model operations. * Cloud-native software development, enterprise application architecture, APIs, integration patterns, data platforms, and analytics ecosystems. * DevSecOps, CI/CD, automated testing, software supply-chain controls, production reliability, and secure development lifecycle practices. * Responsible AI, AI governance, privacy, security, model risk, data governance, and human oversight. * Product-oriented delivery using measurable outcomes, iterative development, user feedback, adoption measures, and value realization., * Advanced degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Business, or a related field. * Leadership experience in professional services, financial services, regulated industries, or enterprise B2B software. * Experience with Microsoft Azure AI, Azure OpenAI, Microsoft Copilot technologies, and modern AI engineering frameworks. * Relevant credentials in AI, cloud architecture, software engineering, cybersecurity, or product delivery. * Experience commercializing proprietary software or creating client-facing digital products and technology-enabled services. ## Description AI strategy and portfolio leadership * Define and execute a multi-year AI software engineering strategy aligned with firm business and technology objectives. * Establish the vision, roadmap, investment priorities, and operating model for the GDC AI development capability. * Identify opportunities to create proprietary software assets that improve client service, quality, efficiency, growth, and professional experience. * Advise executive leaders and governance forums on opportunities, investments, risks, dependencies, and portfolio performance., * Recruit, organize, develop, and retain a multidisciplinary team of AI engineers, software engineers, machine learning engineers, technical leads, and architects. * Define job architecture, role expectations, career paths, leadership spans, technical communities, and talent-development practices for the AI engineering function. * Create an engineering culture centered on innovation, responsible experimentation, accountability, inclusion, collaboration, and disciplined production delivery. * Build a sustainable talent pipeline and develop future engineering leaders. Product and solution delivery * Lead AI-enabled products, platforms, and solutions from discovery and architecture through engineering, release, adoption, operation, and continuous improvement. * Partner with Product Management and business stakeholders to translate strategic priorities into sequenced, funded, and measurable delivery commitments. * Manage execution across concurrent initiatives while balancing value, speed, quality, security, risk, reliability, and maintainability. * Establish outcome, delivery, quality, adoption, and operational measures; use results to improve priorities and execution. Architecture, engineering, and operations * Establish development standards, reference architectures, engineering frameworks, reusable components, testing practices, and technical quality controls. * Define patterns for large language model integration, agentic systems, retrieval-augmented generation, intelligent workflow automation, observability, evaluation, and model operations. * Implement modern cloud-native engineering, DevSecOps, CI/CD, API, integration, reliability, incident-management, and lifecycle-management practices. * Promote reuse, platform leverage, technical-debt management, and reduction of unnecessary technology redundancy. Governance, risk, and stakeholder leadership * Embed security, privacy, legal, regulatory, records-management, data-governance, and responsible AI requirements throughout the development lifecycle. * Partner with Enterprise Architecture, Information Security, Data & Analytics, Risk, Legal, and other control functions to govern design and deployment decisions. * Communicate complex technical matters in clear business terms and build trusted relationships with executives, professionals, clients, vendors, and strategic partners. * Drive adoption, change leadership, knowledge transfer, and transparent reporting of progress, risks, decisions, and realized value. The duties listed are intended to describe the general nature and level of work performed. They are not an exhaustive list of every responsibility, duty, or skill required. Responsibilities may change based on business needs.